Gitnux/Report 2026

AI ML Oil and Gas Industry Statistics

With AI and digital oilfield budgets climbing alongside a steady push to cut methane and downtime, this page spotlights what progress looks like right now, including cloud spending of $679 billion in 2024 and an expected 30% growth in AI adoption from 2024 to 2030. It also sets the stakes in sharp contrast, from methane emissions falling only 33% by 2030 to AI modeled gains that can improve drilling and forecasting, plus leak detection and repair that can cut emissions by 45–70%.
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November 10, 2026Updated
AI ML Oil and Gas Industry Statistics
Daniel VargaWritten by Daniel Varga·Edited by Rebecca Hargrove·Fact-checked by Rajesh Patel
Published ·Updated
How this report is researched
01Editorial Scope

Gitnux editors write the brief first: the topic, the inclusion rules, and the frame for what belongs.

02Source Research

Agents search the published record and log every extracted figure back to the passage it came from.

Read our full methodology →

Search agents attach provenance to every extracted figure.

Next review: within 39 days

01 Industry overview

The ai ml oil and gas industry at a glance

The AI in oil and gas market is estimated at $4.28 billion in 2026 and is expanding as operators deploy machine learning for predictive maintenance, drilling, subsurface analysis and process optimization.

$4.28B[21]
Market size, 2026
13.03%[21]
Forecast CAGR, 2026–2031
49.20%[21]
ML share of revenue, 2025
33%[66]
Oil and gas AI deployment, 2026
Market size
The global AI in oil and gas market was valued at $3.79 billion in 2025 and is estimated at $4.28 billion in 2026. The market includes AI and machine-learning software, platforms and services used across exploration, production, refining, transportation and related operations.[21]
Forecast & CAGR
The market is forecast to reach $7.91 billion by 2031, representing a 13.03% compound annual growth rate from 2026 to 2031. Asia-Pacific is projected to be the fastest-growing region, with a 14.41% CAGR over the same period.[21]
Market share & regions
North America accounted for 35.95% of global AI in oil and gas revenue in 2025, making it the largest regional market. Asia-Pacific is identified as the fastest-growing region, supported by upstream investment and expanding digitalization in China, India and Southeast Asia.[21]
Segmentation
Upstream represented 61.05% of market share in 2025, while onshore operations represented 63.10%. Predictive maintenance was the largest application at 37.60% of spending, and machine learning accounted for 49.20% of revenue by AI technique.[21]
Key players & concentration
The market is assessed as having medium concentration, with oilfield-service companies, specialist technology vendors and cloud providers competing through integrated AI platforms and sector-specific tools. RSM found that 50% of verified AI implementations in its energy-company sample used external vendor platforms, indicating a substantial role for third-party providers.[21][66]
Pricing
Services accounted for 65.80% of AI in oil and gas market revenue in 2025, indicating that implementation, integration and managed services remain larger than platform revenue. Platform revenue is forecast to grow at a 13.74% CAGR through 2031, while the report does not provide a reliable standardized per-user or per-site price.[21]
Trends, drivers & challenges
Growth is being supported by predictive maintenance, real-time drilling and hydraulic-fracturing optimization, autonomous operations, edge analytics and methane-leak monitoring. Key constraints include data quality, workflow integration, governance, cybersecurity and limited availability of workers who combine AI expertise with oil and gas domain knowledge; S&P Global reported that only 43% of energy AI projects launched in the prior year were expected to deliver ROI within 12 months.[21][5][43]
Consumer behavior & competition
AI demand is concentrated in operational and analytical use cases with measurable reliability, cost and productivity benefits rather than consumer-facing applications. In L.E.K.'s 2025 survey, more than half of oil and gas companies used AI for market-sentiment modeling and performance analytics, while adoption was more selective for field-level automation; competition is increasingly based on domain data, integration capability and trusted deployment at scale.[43][21]
Latest news
Recent industry analysis indicates that oil and gas companies are moving from isolated pilots toward broader AI deployment, but returns remain uneven. S&P Global reported in 2026 that 35% of surveyed energy and utilities companies had generative AI fully integrated across the organization, while 27% had fully integrated agentic AI; those shares were expected to rise to 49% and 38%, respectively, within 12 months.[5]

02 Ranking

Top 10 Companies in the Ai Ml Oil And Gas Industry

The leading companies in the ai ml oil and gas industry, ranked on market impact, performance, momentum, innovation and brand popularity.

Updated November 10, 2026 · Next review December 10, 2026

Verdict

KONGSBERG leads Gitnux's 2026 ai ml oil and gas ranking with an Overall score of 9.1, with Falkor unit supports AI-enhanced decision support; R&D is 7.4% of revenue. Enverus is the highest-ranked analytics and data platform at 9.0, driven by Enverus AI trained on proprietary energy data covering millions of wells.

Ranked by Overall score · How we score ↓

Editor's top 3 picks

Where to start, by need.

  1. #1 Overall · Leader in digital oilfield AI

    KONGSBERG

    NO

    Ranked #1 · 9.1 · Digital oilfield AI

    Why we picked it: It pairs industrial software for energy/process decision support with R&D spending tracked at 7.4% of revenue.

    See in ranking ↓Visit kongsberg.com ↗

  2. Leader in energy data & AI

    Enverus

    US

    Ranked #2 · 9.0 · Energy data & AI

    Why we picked it: It focuses on energy-data and domain-trained AI, with training tied to proprietary coverage of millions of wells.

    See in ranking ↓Visit enverus.com ↗

  3. Leader in industrial AI analytics

    Seeq

    US

    Ranked #4 · 8.8 · Industrial AI analytics

    Why we picked it: It targets operational analytics for oil and gas users through industrial AI and analytics software.

    See in ranking ↓Visit seeq.com ↗

Pick 1 is the #1 company overall. Picks 2 and 3 are the highest-ranked companies in the top 5 that add a new segment.

Full ranking

Sort by
Top 10 companies in the ai ml oil and gas industry, 2026 – Gitnux ranking
#CompanyOverall ↓Company snapshot
1

KONGSBERG

Digital oilfield AI · NO

9.1
2

Enverus

Energy data & AI · US

9.0
3

SLB

Digital oilfield AI · US

8.9
4

Seeq

Industrial AI analytics · US

8.8
5

Baker Hughes

Predictive maintenance & optimization · US

8.8
6

Corva

AI/ML drilling and completion analytics · US

8.7
7

Halliburton

Landmark AI and ML software for E&P · US

8.6
8

C3 AI

Enterprise AI applications for oil and gas · US

8.1
9

Senzit

AI/ML predictive maintenance for oil and gas, Brazil · BR

8.1
10

Petrolink

Real-time drilling data and analytics for oil and gas · US

8.1

How we score

  • Impact (30%): scale and weight in the market (revenue, market share, employees, market position).
  • Performance (20%): recent revenue growth and operating margin; an analyst assessment where a company does not publish them.
  • Momentum (20%): growth trajectory and future potential.
  • Innovation (15%): R&D intensity and how technologically advanced the company is.

Popularity (brand search demand, 15%) counts toward Overall but is not published as a column. Overall leaves out what is not known.

Scale Scores are shown on a 7–10 scale with one decimal; bars start at 7.0.

Cadence Recalculated when new figures are published; next review December 10, 2026.

Independence. Rankings are editorially independent. Scores, ranks, picks and the verdict follow the method above; no company can pay to change them.

Cite this ranking

APA

Daniel Varga. (2026, February 13). Top 10 companies in the ai ml oil and gas industry. In AI ML Oil And Gas Industry Statistics. Gitnux. https://gitnux.org/ai-ml-oil-and-gas-industry-statistics/#top-companies

report visual · Projection

Global AI technology market in the oil and gas sector

Global AI technology market in the oil and gas sector grew from $2.5B in 2022 to $3.1B in 2024.

$2.5B
Start
+12.25%
CAGR · 2y
$3.1B
Projected
20222024
source-verifiedbccresearch.com2028

04 Statistics

Technology & Innovation

Technology adoption centers on analytics customization and specific operational use cases like predictive maintenance and process automation, with less than 25% beyond AI pilot phases in 2025.

AI in the ai ml oil and gas industry

  • 56% of reservoir-engineering respondents used custom oil and gas analytics solutions in 2024.[1]
  • 41% of surveyed U.S. oil and gas firms using or planning AI cited predictive maintenance as a use case in 2024.[2]
  • 41% of surveyed U.S. oil and gas firms using or planning AI cited geology or reservoir engineering as a use case in 2024.[2]
  • 10% of oil and gas employees surveyed globally said they would use AI in their role within six months in 2024.[3]
  • 44% of surveyed U.S. oil and gas firms using or planning AI cited process automation as a use case in 2024.[2]
  • 47% of oil and gas professionals surveyed by DNV said their organizations would use AI in operations in 2024.[4]

Automation in the ai ml oil and gas industry

  • 75% of surveyed upstream and downstream energy companies identified predictive maintenance as an AI use case in 2026.[5]
  • 37.4% of Canadian oil and gas firms implemented clean technologies and advanced security systems in 2022.[6]
  • 42.0% of Canadian oil and gas firms used business-intelligence tools in 2022.[6]
  • Less than 25% of surveyed oilfield-services and equipment companies had progressed beyond AI pilot phases in 2025.[7]
  • 59.6% of Canadian oil and gas extraction firms reported adopting advanced technologies in 2022.[6]
  • 71.5% of oil and gas employers expected workforce automation to be an important strategy through 2030.[8]

Digital Transformation in the oil and gas sector (sector benchmark)

  • More than 60% of upstream oil and gas survey respondents report gains of at least 10% in production, recovery, capital efficiency or time savings from digital solutions.[9]
  • 92% of oil and gas and chemicals companies were implementing, developing or planning new digital-twin applications within five years in 2025.[9]
  • Across oil, gas and utilities worldwide, 17% of companies reported completing their digital-transformation journeys in 2022.[10]
  • 50% of oil and gas and chemicals companies were already using digital twins to manage assets in 2025, according to the EY Future of Energy Survey.[9]
  • Over US$790 billion of value is expected from digital solutions in upstream oil and gas worldwide between 2026 and 2035, based on an EY survey.[9]
  • Over 90% of upstream oil and gas respondents expect their organisation’s digital spending to grow during the five years after 2026.[9]

Technology Adoption in the oil and gas sector (sector benchmark)

  • Among oil, gas and energy organizations surveyed by ASUG, 56% expected AI and machine learning to greatly affect digital-transformation efforts in 2025.[11]
  • Among oil, gas and energy organizations surveyed by ASUG, 42% expected RPA or similar automation to greatly affect digital-transformation efforts in 2025.[11]
  • Among oil, gas and energy organizations surveyed by ASUG, 33% expected low-code or no-code technology to greatly affect digital-transformation efforts in 2025.[11]
  • Among oil, gas and energy organizations surveyed by ASUG, 19% expected conversational user interfaces or chatbots to greatly affect digital-transformation efforts in 2025.[11]

Cloud Computing in the oil and gas sector (sector benchmark)

  • Across oil and gas companies surveyed globally, 45% ranked the cloud among technologies delivering high business benefit over the following 18 months.[12]
  • Across oil and gas companies surveyed globally, 90% reported using cloud computing in 2020, while 76% of users were developing improvements or new use cases.[13]
  • Across oil and gas companies surveyed globally, 49% ranked the cloud as a high-investment technology over the following 18 months.[12]
  • Among oil, gas and energy organizations surveyed by ASUG, 47% expected cloud technology to greatly affect digital-transformation efforts in 2025.[11]

Big Data in the oil and gas sector (sector benchmark)

  • Across oil and gas companies surveyed in the 2016 Accenture upstream study, 13% used big-data and analytics insights to shape their approach to markets and competitors.[14]
  • Across oil and gas organizations surveyed globally, 59% ranked big data and analytics among technologies delivering high business benefit over the following 18 months.[12]
  • Across oil and gas companies surveyed in the 2016 Accenture upstream study, 36% were already investing in big data and analytics.[14]
  • Across oil and gas organizations surveyed globally, 52% ranked big data and analytics as a high-investment technology over the following 18 months.[12]

05 Statistics

Data & Analytics

Data integration and standardized capture show measurable throughput gains, including 48 hours for reservoir insights versus two to four weeks.

Business intelligence in the ai ml oil and gas industry

  • 48 hours was the time required to deliver reservoir insights for a Southeast Asian operator using SLB digital workflows, compared with the usual two to four weeks.[15]
  • 2,100-plus active wells were covered by PEMEX’s Active Tertiary Gulf Oil production-monitoring and data-management program in Mexico.[15]
  • More than 90% of global production was simulated or modelled using at least one SLB digital solution in 2026.[16]
  • 1,224 metres per day was the rate-of-penetration record achieved by Eni after connecting SLB’s digital drilling and well-execution solutions.[15]
  • More than 45 million CPU hours were consumed by SLB’s platform in the first quarter of 2026 for customer simulation, processing and AI workloads.[16]
  • More than 2 billion API calls were recorded across SLB’s digital platform in 2025, indicating machine-to-machine and workflow activity supporting energy-sector analytics.[16]

Data analytics in the ai ml oil and gas industry

  • 1,167 metric tons of CO2 emissions were avoided by Petrobras through real-time monitoring and automated flowback analytics during well cleanup.[17]
  • 80% less data-capture and processing time was reported at PEMEX after operational databases, standardized data capture and automated reporting were introduced.[15]
  • 100,000 times faster than conventional simulation were Shell’s AI-based subsurface analytics for screening carbon-storage geologies, injection rates and pressures.[18]
  • 90% lower water-handling-related production losses were achieved by an Ecuador operator after applying edge analytics and machine learning.[15]
  • 60% higher data quality was reported at PEMEX after production-data validation and standardization were implemented in Mexico.[15]

Data Quality in the oil and gas sector (sector benchmark)

  • 68% of upstream oil and gas professionals rated data integrity as important for effective and efficient geoscience analysis in 2023.[5]
  • 66% of upstream oil and gas professionals rated accurate data as important for effective and efficient geoscience analysis in 2023.[5]
  • 36% of geoscience professionals said having the most current and best-quality data was their biggest collaboration challenge in 2023, across oil and gas and other surveyed industries.[19]
  • 12% of oil and gas respondents lacked the necessary information for data-driven decisions in 2023, across the surveyed industries worldwide.[19]
  • 62% of upstream oil and gas professionals rated complete data as important for effective and efficient geoscience analysis in 2023.[5]
  • 50% of oil and gas respondents were confident their organization could handle data-quality issues in 2023, based on a global geoscience-sector survey.[19]

Data Governance in the oil and gas sector (sector benchmark)

  • 40% of upstream oil and gas organizations considered data management a strategic priority in 2023, up from more than 30% in 2020.[5]
  • 56% of upstream oil and gas organizations identified data security as a data-management priority for the next two years in 2023.[5]
  • 14% of oil and gas respondents had an established data-management framework in 2023, one of the lowest rates among surveyed industries worldwide.[20]
  • 4% of upstream oil and gas organizations reported that geoscience data management received only minimal senior-management and wider-business support in 2023.[5]
  • 96% of upstream oil and gas organizations identified data centralization as a current or future data-management priority for the next two years in 2023.[5]
  • 32% of oil and gas respondents expected to establish a data-management framework within two to three years in 2023.[20]

KPIs in the ai ml oil and gas industry

  • 32% was the rate-of-penetration improvement compared with offset wells in Halliburton’s Malaysian AI-enabled brownfield project.[17]
  • 14 hours of rig time were saved in one Malaysian well section, a tracked efficiency KPI for Halliburton’s AI-enabled drilling deployment.[17]
  • 82% was the maximum reduction in manual unloading, a tracked operating KPI, across 10 U.S. gas wells using SLB’s Agora solution.[15]
  • 51 shutdowns were reduced in a Canadian ESP deployment tracked by Halliburton’s Intelevate production service.[17]
  • More than 30% was the improvement in well-delivery time tracked in Halliburton’s European ML-enabled autonomous-drilling pilot.[17]
  • 43.6% was the average rate-of-penetration improvement tracked across four well sections in Halliburton’s European autonomous-drilling pilot.[17]

06 Statistics

Strategy & Growth

Spending shifts toward measurable ROI: predictive maintenance remains the largest application at 37.60%, while machine learning accounts for 49.20% of 2025 revenue.

Growth in the ai ml oil and gas industry

  • 14.68% is the projected CAGR for deep-learning methods in the AI-in-oil-and-gas market through 2031.[21]
  • $790 billion is the estimated value that digital solutions could add to upstream oil and gas from 2026 through 2035.[9]
  • 37.60% was the share attributed to predictive maintenance in the AI-in-oil-and-gas market in 2025.[21]
  • 13.74% is the projected CAGR for AI platform revenues in the AI-in-oil-and-gas market through 2031.[21]
  • 14.12% is the projected CAGR for downstream applications in the AI-in-oil-and-gas market through 2031.[21]
  • 13.85% is the projected CAGR for offshore applications in the AI-in-oil-and-gas market through 2031.[21]

Differentiation in the ai ml oil and gas industry

  • 5% was the reduction in lease operating expenses recorded by Chevron’s Kaybob asset after deploying OPX Ai’s AI-based operations service.[22]
  • 5% is the reported increase in operating efficiency from RoboWell at ADNOC’s offshore NASR field.[23]
  • 71,000 BOE of deferred production was estimated to have been avoided during a 12-month Chevron AI deployment.[22]
  • 30% is the maximum reported optimization in gas-lift consumption delivered by ADNOC’s RoboWell AI solution.[23]
  • 49.20% was the share attributed to machine-learning approaches in 2025 AI-in-oil-and-gas market revenue.[21]

Mergers and acquisitions in the ai ml oil and gas industry

  • $17.1 billion was the announced value of ConocoPhillips’ all-stock acquisition of Marathon Oil in 2024.[24]
  • $350 million was the reported acquisition value of Dropbox’s purchase of Tigris Data, an AI-workload storage provider, though the transaction was outside the oil-and-gas operating sector.[25]
  • 87% of North American oilfield-services transactions in the referenced fourth quarter of 2024 were concentrated in oil and gas equipment and services.[26]
  • $4.9 billion was the announced value of ExxonMobil’s acquisition of Denbury Resources in 2023, adding carbon-capture and storage capabilities.[24]

Strategic partnerships in the ai ml oil and gas industry

  • One joint innovation laboratory was planned by TotalEnergies and Mistral AI under their 2025 artificial-intelligence collaboration.[27]
  • SLB secured exclusive access to deploy Geminus AI’s physics-informed AI model builder for oil and gas operations under a 2024 investment and technology partnership.[15]
  • $340 million was the value of AIQ’s three-year contract with ADNOC to deploy ENERGYai and related AI solutions across upstream operations.[28]
  • Up to four gigawatts of gas-fired power-generation capacity was planned by Chevron, Engine No. 1 and GE Vernova for data centers supporting AI demand.[24]

07 Statistics

People & Workforce

Workforce constraints show up as retention and skills gaps; 76% of energy and utilities employers reported a talent and skills gap in 2024.

Hiring in the ai ml oil and gas industry

  • 33% of energy companies reported losses during the prior year because of failure to attract talent in 2025.[29]
  • 76% of energy and utilities employers reported a talent and skills gap in 2024.[29]
  • 60 Ukrainian oilfield workers were placed with 16 North Dakota employers between July and November 2023.[24]
  • Around two-thirds of oil and gas supply workers were estimated to have base skills transferable to other energy sectors with targeted retraining in 2025.[30]

Employee retention in the ai ml oil and gas industry

  • 5.7 years was the median employee tenure reported for U.S. mining and oil-and-gas workers in January 2024.[31]
  • 81% of oil and gas and chemicals respondents reported a lack of dedicated personnel for worker-retention efforts in 2024.[9]
  • 72% of oil and gas and chemicals respondents cited insufficient senior-leadership support for worker-retention efforts in 2024.[9]
  • Organizations in the petroleum and chemical industries reported rising turnover and declining retention in APQC’s 2024 employee-experience research.[32]
  • 52% of energy employees in EY’s 2024 survey were likely to quit, including oil and gas employees.[9]

Remote Work in the oil and gas sector (sector benchmark)

  • Across U.S. oil and gas extraction, 40.0% of positions were fully hybrid in 2024, based on five employer respondents.[33]
  • Across U.S. oil and gas extraction, 60.0% of positions were fully in person in 2024, based on five employer respondents.[33]
  • Oil and energy accounted for 31.1% of new remote job postings in 2024, while remote postings declined 17.3% from 2023.[34]
  • Across Canada’s mining, quarrying, and oil and gas extraction sector, 2.3% of employees were expected to work entirely remotely in the fourth quarter of 2025.[35]
  • Energy, oil and gas offices averaged 9.9% capacity usage in 2024, indicating substantially less office attendance than full-time in-person operation.[36]

Hybrid Work in the oil and gas sector (sector benchmark)

  • Among U.S. energy, natural-resources, and chemicals workers, 43% preferred mostly in-office work in KPMG’s 2025 survey.[37]
  • Across U.S. oil and gas extraction employers, 40% of surveyed positions were fully hybrid in 2023, based on five respondents.[33]
  • Across Canada’s mining, quarrying, and oil and gas extraction sector, 36.0% of employees were expected to work partly on-site and partly remotely in the fourth quarter of 2025.[35]
  • Among U.S. energy, natural-resources, and chemicals workers, 25% preferred mostly remote work in KPMG’s 2025 survey.[37]

Upskilling in the ai ml oil and gas industry

  • Around two-thirds of oil and gas supply workers were estimated to have transferable base skills after targeted retraining in 2025.[30]
  • 64% of oil and gas employers planned to reskill and upskill existing workers to work alongside AI.[38]
  • 43% of oil and gas employers identified effective reskilling and upskilling as a workforce priority in 2025.[38]
  • 96% of oil and gas employers planned to prioritize upskilling their workforce, according to the 2025 Future of Jobs survey.[8]
  • 58% of reservoir-engineering respondents observed a moderate change in required skills because of AI in 2024.[1]

Employee Engagement in the oil and gas sector (sector benchmark)

  • Globally, 20% of employees were engaged in 2025, according to Gallup’s 2026 global report.[39]
  • Among oil, energy, and utilities employees, the engagement rate was 86% in the 2022–23 OE Engagement Report.[40]
  • At California Resources Corporation, 86% of employees agreed in 2023 that they could depend on members of their team.[41]
  • At California Resources Corporation, 87% of employees agreed in 2023 that the people they work with treat each other with respect.[41]
  • At California Resources Corporation, 76% of employees agreed in 2023 that their job gave them flexibility to meet work and personal needs.[41]

DEI in the ai ml oil and gas industry

  • Women made up about one-fifth of the global energy workforce in 2024, with only marginal gains in oil and gas supply.[30]
  • 21% of the Energy Workforce & Technology Council’s workforce was female in 2023, up from 19% in 2021.[42]

08 Statistics

Customer & Sales

Customer-focused AI usage concentrates on service management and experience, with 48% of energy executives applying AI in 2025.

Customer experience in the ai ml oil and gas industry

  • 48% of energy executives reported applying AI to customer experience and service management in the 2025 Global Energy Study.[43]
  • 80% of oilfield-services and equipment leaders believed generative AI value would be driven partly by personalized service and improved customer experience, according to a 2025 survey.[7]
  • 78.2 was the average enterprise customer-experience score for oil-and-gas services providers in ISG research through December 2024, based on 112 responses in global markets.[44]
  • 32% of non-digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at improving satisfaction or experience in the 2020 CIO Survey.[45]
  • 40% of digital-leading oil-and-gas organizations were very or extremely effective at scaling digital channels to meet new customer demands and expectations in 2020.[45]

Customer loyalty in the ai ml oil and gas industry

  • 30% of digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at increasing customer loyalty in the 2020 CIO Survey.[45]
  • 21% of non-digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at increasing customer loyalty in the 2020 CIO Survey.[45]

09 Statistics

Marketing & Brand

Brand-facing analytics remains marginal versus operational analytics, while digital customer offerings drive reported effectiveness in 2020 survey results.

Marketing in the ai ml oil and gas industry

  • 64% of digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at helping customers embrace their brand in 2020.[45]
  • 20% of non-digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at increasing company worth in 2020.[45]
  • 32% of non-digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at increasing revenues in 2020.[45]
  • 45% of digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at increasing revenues in 2020.[45]
  • 33% of non-digital-leading oil-and-gas organizations said digital customer offerings were very or extremely effective at helping customers embrace their brand in 2020.[45]

Digital marketing in the ai ml oil and gas industry

  • 32% of oil-and-gas respondents reported high benefits from digital marketing and consumer insights in a 2022 digital-transformation survey.[12]
  • 31% of oil-and-gas respondents reported high investment in digital marketing and consumer insights in a 2022 digital-transformation survey.[12]

Social media in the ai ml oil and gas industry

  • 12% of oil-and-gas respondents identified social networks as a high-benefit technology area in a 2022 digital-transformation survey.[12]
  • 14% of oil-and-gas respondents identified social networks as a high-investment technology area in a 2022 digital-transformation survey.[12]

Influencer Marketing in the oil and gas sector (sector benchmark)

  • Shell’s official Instagram account averaged 572 likes per post among five leading European oil and gas companies analyzed over 2019–2022.[46]
  • At least 17 influencers promoting BP’s rewards app generated 675,000 likes and 5 million views across posts, according to analysis of campaigns through 2023.[47]
  • More than 100 influencers worldwide had been paid to promote fossil-fuel firms since 2017, in campaigns reported to have reached billions of people, according to a 2023 investigation.[47]
  • One Shell influencer campaign made audiences 31% more likely to believe the company was committed to cleaner fuels, according to a campaign submission cited in 2023.[47]
  • Shell’s official Instagram account averaged 183 comments per post among five leading European oil and gas companies analyzed over 2019–2022.[46]

10 Statistics

Operations & Execution

Operational gains come from drilling optimization, automated setpoints, and real-time operations, including 90% reduction in optimization-cycle time in a 26-well pilot.

Process optimization in the ai ml oil and gas industry

  • 33% higher rate of penetration was achieved in three Colombian horizontal wells using machine-learning-assisted drilling optimization.[17]
  • 90% reduction in optimization-cycle time was achieved in a 26-well U.S. Permian pilot using AI recommendations for electric-submersible-pump setpoints.[16]
  • 10% higher production per well was achieved in a U.S. Permian pilot using automated AI optimization of electric-submersible-pump operations.[16]
  • 30% lower gas-lift consumption was achieved after AIQ and Halliburton implemented RoboWell in ADNOC upstream operations.[17]
  • 10% higher gas-well production and 80% fewer manual interventions were achieved with autonomous AI setpoint optimization in the United States.[15]

Operational efficiency in the ai ml oil and gas industry

  • Nearly 1,500 offshore staff-days per rig per year were eliminated in Gulf of Mexico operations through digitally enabled remote expertise.[15]
  • More than 50% reduction in weight-to-weight time was achieved by Eni using connected digital drilling workflows.[15]
  • 30% to 40% cycle-time reductions have been reported for AI-augmented oil-and-gas engineering workflows, according to SLB.[15]
  • 30% to 40% less engineering effort was reported after ADNOC deployed an AI-enabled real-time operations center across more than 120 drilling rigs in the UAE.[15]
  • 48-hour reservoir insights replaced conventional analysis requiring two to four weeks for a Southeast Asian operator using digital workflows and analytics.[15]

Quality management in the ai ml oil and gas industry

  • Production downtime linked to electric-submersible-pump performance fell from 24 hours to 2 hours per event for an Oman operator using remote surveillance.[15]
  • 70% lower anomaly downtime was reported for wells using SLB’s Rod Lift Advisor application, which applies AI and machine learning to oilfield operations.[15]
  • Approximately 30% of electric-submersible-pump shutdowns were eliminated for HydroCarbon Finder E&P in Oman using remote monitoring and optimization.[15]
  • 33% higher drilling rate of penetration was achieved while improving trajectory accuracy and hole-profile smoothness in three Colombian wells using AI-assisted drilling.[17]
  • 15% to 45% improvement in casing and liner running speed was achieved while reducing deviation from planned well paths in Colombia.[17]

Supply chain in the ai ml oil and gas industry

  • 23% of surveyed oil-and-gas professionals identified safety and inspection improvement as an AI use case.[3]
  • 25% of surveyed energy professionals reported using AI for automated workflow and workplace collaboration, including oil-and-gas applications.[3]
  • 17% of surveyed petrochemicals and oil-and-gas professionals reported using machine learning, a core technology for supply-chain forecasting and optimization.[3]
  • 20% of surveyed energy professionals reported using predictive analytics to understand future energy demand, supporting oil-and-gas supply planning.[3]
  • 18% of surveyed energy professionals reported using remote monitoring and automation, including oil-and-gas field and logistics operations.[3]

Outsourcing / Offshoring in the oil and gas sector (sector benchmark)

  • Among supplementary outsourced or offshore resources in oil, gas and energy, 27% handled accounts payable or receivable in 2022.[48]
  • Among supplementary outsourced or offshore resources in oil, gas and energy, 81% focused on contract administration in 2022.[48]
  • Among supplementary outsourced or offshore resources in oil, gas and energy, 48% handled contract review and discovery in 2022.[48]
  • Across oil, gas and energy organizations, 16% used offshore resources for contract-commercial-management activities in 2022.[48]
  • Across oil, gas and energy organizations, 24% had fully outsourced some contract-commercial-management activities in 2022.[48]

11 Statistics

Finance & Pricing

The value case ties to recurring and structural benefits; services accounted for 65.80% of revenue in 2025, supporting ongoing integration and managed delivery.

Pricing strategy in the ai ml oil and gas industry

  • 4% to 8% peer-relative cost advantage is associated with strategic procurement and supply-chain capability, improving oil-and-gas operators’ pricing resilience.[49]
  • Approximately 900,000 barrels of oil equivalent per day was Chevron’s Permian Basin production in the third quarter of 2024.[50]
  • Below $50 per barrel Brent is Chevron’s targeted capital-and-dividend breakeven through 2030.[50]
  • 2% to 3% annual oil-and-gas production growth through 2030 is Chevron’s stated plan, supporting pricing and volume strategy.[50]
  • $74,800 of additional annual oil production per well was enabled by reducing electric-submersible-pump downtime for HydroCarbon Finder E&P in Oman.[15]

Cost reduction in the ai ml oil and gas industry

  • 5% reduction in lease operating expense was achieved through AI-based artificial-lift and flow-assurance deployments at Chevron and ConocoPhillips fields.[22]
  • 41% to 63% lower pump downtime per pad was achieved in Saudi Arabia after deploying an intelligent pump-idling system during fracturing operations.[15]
  • 30% fewer field visits per month were achieved by a Permian operator using AI-enabled remote management of multiphase flowmeters.[15]
  • 3% higher production across 20 wells generated 14,000 additional barrels in the first year for a Permian operator using AI-enabled flowmeter optimization.[15]
  • More than 3% lower annual fleet fuel consumption was achieved by intelligent pump-idling controls during Saudi Arabian fracturing operations.[15]

Profitability in the ai ml oil and gas industry

  • $230 billion in potential annual value is estimated from AI use cases across upstream oil and gas and oilfield-services companies at full maturity.[7]
  • 900,000 barrels of oil equivalent per day was Chevron’s record Permian Basin production in the third quarter of 2024, where AI supported productivity and cycle-time improvements.[50]
  • 10% higher production per well was reported in an AI optimization pilot covering 26 Permian wells, supporting higher upstream revenue potential.[16]
  • 3% higher production across 20 Permian wells produced an additional 14,000 barrels during the first year of AI-enabled flowmeter optimization.[15]
  • 20% higher vertical rate of penetration and 65% fewer premature drilling-motor failures improved economics for ConocoPhillips, saving $30,000 per well.[51]

Financial planning in the ai ml oil and gas industry

  • $65 billion in annual recurring near-term AI value is McKinsey’s estimate for upstream oil-and-gas use cases, informing investment prioritization.[7]
  • More than 3% improvement in return on capital employed is Chevron’s target by 2030 at $70 Brent.[50]
  • 2% to 3% annual oil-and-gas production growth through 2030 is included in Chevron’s capital and portfolio plan.[50]
  • $230 billion in annual full-potential AI value is McKinsey’s estimate for upstream oil and gas and oilfield-services companies.[7]
  • $3 billion to $4 billion of structural cost reductions is Chevron’s target by the end of 2026.[50]

12 Statistics

Product & Service

Product deployments emphasize operational telemetry and automation, including 2 billion API calls on a digital platform in 2025 and millions of daily predictions.

Product development in the ai ml oil and gas industry

  • 43.6% was the average rate-of-penetration improvement across four well sections in a European autonomous-drilling pilot using physics-based modelling and machine learning.[17]
  • 100,000 times faster than conventional physics-based simulation were Shell’s AI models for evaluating carbon-dioxide storage in subsurface reservoirs.[18]
  • 87.4% of footage was drilled autonomously in an Ecuador horizontal-well field trial using AI/ML-driven geosteering and automated directional drilling.[17]
  • 97% of an 8½-inch wellbore section in Malaysia was autonomously steered using Halliburton’s AI- and ML-enabled LOGIX technology in a brownfield redevelopment project.[17]
  • 8 fields had ADNOC’s AI-powered Production System Optimization platform deployed in 2025, with full deployment across 25 onshore and offshore fields planned by 2027 in the United Arab Emirates.[15]

Product lifecycle in the ai ml oil and gas industry

  • 20 billion rows of data from 3 million sensors were ingested weekly by Shell’s C3 AI predictive-maintenance program in 2024.[52]
  • More than 10,000 pieces of equipment were covered by Shell’s scaled predictive-maintenance program across upstream, manufacturing and integrated-gas assets in 2024.[52]
  • 90% lower production losses related to water handling were reported for an Ecuador operator after edge computing, data analysis and machine learning improved failure prediction and response.[15]
  • More than 15 million predictions were generated every day by Shell’s C3 AI predictive-maintenance deployment in 2024.[52]
  • 20,000-plus pieces of critical equipment were monitored globally by Shell’s AI predictive-maintenance program, according to a 2024 case study.[52]

After-sales in the ai ml oil and gas industry

  • 10.5 additional days of ESP uptime were achieved in a Canadian customer deployment of Halliburton’s Intelevate production-optimization service.[17]
  • 135,000 MCF of additional gas production was projected over one year from an SLB Agora edge-AI deployment across 10 U.S. gas wells.[15]
  • 82% was the maximum reduction in manual well unloading achieved across 10 U.S. gas wells using SLB’s Agora edge-AI solution.[15]
  • 15.5 hours of cleanup execution time were reduced for Petrobras through Halliburton’s automated flow-control and solids-management service.[17]
  • 1,500 staff-days offshore per rig per year were eliminated in Gulf of Mexico operations through SLB’s remote digital service delivery.[15]

Value proposition in the ai ml oil and gas industry

  • 1,167 metric tons of CO2 emissions were reduced during Petrobras’s automated well-cleanup campaign using Halliburton technology.[17]
  • 10% higher gas-well production was achieved over a three-month period using SLB’s Agora edge-AI autonomous setpoint optimization in the United States.[15]
  • 26% was the reduction in Petrobras cleanup execution time from automated flow control and integrated solids management in Brazil.[17]
  • 30% faster rate of penetration was achieved against offset wells in a Malaysian brownfield redevelopment using Halliburton’s AI-enabled LOGIX drilling technology.[17]
  • 80% was the reduction in data-capture and processing time at PEMEX’s Active Tertiary Gulf Oil asset after an integrated production-data solution was implemented in Mexico.[15]

13 Statistics

Sustainability & Ethics

AI is increasingly linked to emissions and operational efficiency, with 54% of energy and natural-resources organizations deploying AI in 2026.

Sustainability in the ai ml oil and gas industry

  • 54% of energy and natural-resources organizations had deployed AI in some form in 2026, supporting digital sustainability and operational-efficiency initiatives.[53]
  • 167 billion cubic metres of associated gas were flared at upstream oil-and-gas facilities and LNG plants in 2025, an environmental challenge targeted by monitoring technologies.[54]
  • 95% of energy-sector survey respondents had implemented AI to a large or moderate extent in 2026, including applications for emissions and sustainability tracking.[55]
  • 47% of oil-and-gas professionals surveyed expected their organizations to use AI in operations in 2024.[4]
  • More than 75% of oilfield-services and equipment executives believed generative AI would deliver operational efficiencies in 2025.[7]

ESG in the ai ml oil and gas industry

  • 74% of energy leaders said AI controls existed but that supporting evidence was fragmented in 2026.[56]
  • 57% of energy leaders cited governance or compliance barriers as a cause of AI underperformance in 2026.[56]
  • 64% of energy boards had integrated AI risk into ongoing oversight in 2026.[56]
  • 54% of energy leaders identified regulatory and compliance uncertainty as their top concern about agentic-AI deployment in 2026.[56]
  • 20% of energy, resources and industrials companies had reached at least moderate agentic-AI adoption in 2026.[57]

Carbon footprint in the ai ml oil and gas industry

  • Around 10 million tonnes of methane were emitted from upstream onshore oil-and-gas operations in the United States in 2023, based on satellite observations.[30]
  • Around one-quarter of methane-emission sources detected at oil-and-gas facilities were recurrent sources in Carbon Mapper’s analysis of more than 2,000 plumes.[30]
  • Oil-and-gas ultra-emitting sources detected by satellites would produce just over 10 million tonnes of methane annually, about 15% of global oil-and-gas methane emissions.[30]
  • Oil operations emitted around 50 million tonnes of methane in 2023, while the natural-gas supply chain emitted just under 30 million tonnes.[30]
  • More than 80 million tonnes of methane were emitted by global oil-and-gas operations in 2025.[30]

15 Statistics

Globalization & Expansion

Deployment expansion is planned across fields and geographies, including 25 ADNOC onshore and offshore fields targeted for full AiPSO deployment by 2027.

Globalization in the ai ml oil and gas industry

  • Twenty-five ADNOC onshore and offshore fields are targeted for full AiPSO deployment by 2027.[15]
  • $3.1 billion was spent on AI across the broader global energy sector, including oil and gas and power, in 2024.[64]
  • Eight ADNOC fields had received deployment of the AiPSO AI-powered production-optimization solution by November 2025.[15]
  • $18.5 billion is forecast to be spent on AI across the broader global energy sector by 2028.[64]
  • $230 billion is the estimated annual full-potential value AI could unlock across global upstream oil and gas and oilfield-services companies.[7]

Stakeholder management in the ai ml oil and gas industry

  • 40% lower was the average concentration of AI talent in utilities, oil, gas and mining than in education, financial services, technology, and information and media between 2018 and 2024.[65]
  • 400 energy companies participated in an IEA survey examining AI adoption and expected benefits across the energy industry, including oil and gas.[65]
  • 28% of surveyed petrochemical and oil-and-gas professionals did not know what AI was used for in their companies in the GETI 2024 survey.[3]

Distribution channels in the ai ml oil and gas industry

  • More than 500 wells are planned for expansion of AIQ’s RoboWell autonomous well-control solution after its offshore NASR deployment.[23]
  • More than 2,000 wells are being covered by ADNOC’s extended AI-powered well-digitalization program across its onshore fields.[23]

Key questions about the AI Ml Oil and Gas Industry

What is the 2026 market size for AI and ML in oil and gas?
The global AI in oil and gas market is estimated at $4.28 billion in 2026 (from $3.79 billion in 2025).
How fast is the market expected to grow after 2026?
The forecast CAGR for 2026–2031 is 13.03%, with the market expected to reach $7.91 billion by 2031.
Which companies lead the ai ml oil and gas industry?
KONGSBERG leads Gitnux's 2026 ai ml oil and gas ranking with an Overall score of 9.1, with Falkor unit supports AI-enhanced decision support; R&D is 7.4% of revenue. Enverus is the highest-ranked analytics and data platform at 9.0, driven by Enverus AI trained on proprietary energy data covering millions of wells.
Which AI techniques are driving revenue and which applications draw spending?
Machine learning accounts for 49.20% of revenue in 2025, and predictive maintenance is the largest application at 37.60% of spending.
How is the market structured by where demand is coming from?
Upstream represented 61.05% of market share in 2025, and onshore operations represented 63.10%.
What is the biggest constraint operators report when deploying AI projects?
Constraints include governance and workflow integration plus data quality and cybersecurity; S&P Global reported only 43% of energy AI projects were expected to deliver ROI within 12 months.
Reference

Cite This Report

This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.

APA
Daniel Varga. (2026, February 13). AI ML Oil And Gas Industry Statistics. Gitnux. https://gitnux.org/ai-ml-oil-and-gas-industry-statistics
MLA
Daniel Varga. "AI ML Oil And Gas Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-ml-oil-and-gas-industry-statistics.
Chicago
Daniel Varga. 2026. "AI ML Oil And Gas Industry Statistics." Gitnux. https://gitnux.org/ai-ml-oil-and-gas-industry-statistics.

Sources & references

75 sources cited across this report

  1. novilabs.com
  2. dallasfed.org
  3. 3277184.fs1.hubspotusercontent-na1.net
  4. dnv.com
  5. spglobal.com
  6. fsc-ccf.ca
  7. mckinsey.com
  8. reports.weforum.org
  9. ey.com
  10. ifs.com
  11. cdn.v6.asug.com
  12. publicissapient.com
  13. energiesmedia.com
  14. globalgbc.org
  15. slb.com
  16. investorcenter.slb.com
  17. halliburton.com
  18. shell.com
  19. files.seequent.com
  20. seequent.com
  21. mordorintelligence.com
  22. jpt.spe.org
  23. adnoc.ae
  24. apnews.com
  25. itpro.com
  26. mnp.ca
  27. totalenergies.com
  28. prnewswire.com
  29. cewd.org
  30. iea.org
  31. secondtalent.com
  32. apqc.org
  33. naceweb.org
  34. 26254830.fs1.hubspotusercontent-eu1.net
  35. doi.org
  36. vergesense.com
  37. kpmg.com
  38. san.com
  39. gallup.com
  40. d36ps6jii4qpju.cloudfront.net
  41. crc.com
  42. energyworkforce.org
  43. lek.com
  44. hcltech.com
  45. assets.kpmg.com
  46. mdpi.com
  47. desmog.com
  48. worldcc.com
  49. bcg.com
  50. chevron.com
  51. strategyand.pwc.com
  52. c3.ai
  53. assets.aon.com
  54. worldbank.org
  55. media.thinkbrg.com
  56. grantthornton.com
  57. deloitte.com
  58. fanews.co.za
  59. web-assets.claroty.com
  60. cisco.com
  61. chemtrec.com
  62. fticonsulting.com
  63. investing.com
  64. literature.rockwellautomation.com
  65. iea.blob.core.windows.net
  66. rsmus.com
  67. kongsberg.com
  68. enverus.com
  69. seeq.com
  70. bakerhughes.com
  71. corva.ai
  72. sec.gov
  73. senzit.io
  74. petrolink.com
  75. arxiv.org