According to Precedence Research, the AI in oil and gas market size is expected to be worth USD 25.24 billion by 2034, increasing from USD 7.64 billion in 2025 and is projected to grow at a solid CAGR of 14.2% from 2025 to 2034. Oil and gas firms accelerate their digital transformation, and artificial intelligence (AI) is emerging as a game-changer in the sector.
Ottawa, Aug. 04, 2025 (GLOBE NEWSWIRE) — In terms of revenue, the global artificial intelligence (AI) in oil and gas market was valued at USD 6.69 billion in 2024, grew to USD 7.64 billion in 2025. It is predicted to rise from USD 8.73 billion in 2026 to approximately USD 25.24 billion by 2034. This surge is driven by the need for enhanced safety, predictive maintenance, and cost-efficiency in high-stakes operations.
In terms of CAGR, the market of AI in oil and gas is expanding at a double-digit CAGR of 14.2% from 2025 to 2034. The growing demand for enhancing operational efficiency in the sector drives the market growth.
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Artificial Intelligence in Oil and Gas Market Overview & Insights:
The AI in Oil and Gas Market is rapidly evolving as energy companies seek to enhance operational efficiency, safety, and profitability in a volatile and resource-intensive industry. AI technologies such as machine learning, computer vision, natural language processing, and predictive analytics are being integrated across upstream, midstream, and downstream segments to improve decision-making, reduce human error, and lower costs.
In upstream exploration and drilling, AI is enabling faster seismic data interpretation, reservoir modeling, and real-time drilling optimization. In midstream operations, AI supports predictive maintenance of pipelines, logistics optimization, and failure detection in transport systems. In the downstream segment, AI enhances refining processes, automates quality control, and personalizes customer engagement at retail fuel stations.
AI is no longer a luxury but a necessity in the oil and gas industry,” said Shivani Zoting, Principal Consultant at Precedence Research. “With growing pressure to optimize costs and reduce downtime, companies are aggressively adopting AI for predictive maintenance and operational automation.
How is AI Being Utilized in Oil & Gas Industry?
Artificial intelligence in the oil & gas sector is a process of using AI technologies in the oil & gas sector to enhance sustainability, efficiency, and safety. Artificial intelligence is used in various processes like distribution, exploration, refining, and many more in the oil & gas sector. AI helps in identifying potential oil & gas reserves by analyzing seismic data.
AI is widely used in various applications like defect detection, drilling optimization, and supply chain optimization in the oil & gas sector. AI helps in increasing efficiency, enhancing productivity, reducing energy consumption, improving safety, making better decisions, and enhancing sustainability.
Artificial Intelligence Use in Oil & Gas Companies
️ Case Study: Chevron’s Predictive AI Transformation in Oilfield Operations
Chevron Corporation, a global energy giant, has long been at the forefront of technological innovation in the oil and gas sector. Faced with challenges including fluctuating oil prices, aging infrastructure, and a mandate for enhanced safety and environmental compliance, Chevron invested heavily in Artificial Intelligence starting in 2021. Their goal: to transform upstream operations through predictive insights, real-time monitoring, and optimization.
AI Deployment Strategy
Chevron partnered with tech leaders such as Microsoft (Azure AI) and C3.ai to build an enterprise AI platform capable of ingesting real-time data from thousands of IoT sensors embedded across its wellheads, compressors, pipelines, and rigs.
Key AI Technologies Used:
Predictive Maintenance via machine learning to detect early signs of equipment wear and tear.
Digital Twin Models for simulating oilfield performance.
Natural Language Processing (NLP) to extract insights from unstructured technical reports.
Deep Learning Algorithms to enhance seismic data interpretation and reservoir modeling.
Implementation and Impact
Pilot Deployment:
In 2022, Chevron deployed AI-driven predictive maintenance at its Permian Basin field—a highly productive but complex oilfield in Texas.
Measurable Outcomes:
Reduced unplanned equipment downtime by 25% in the first year.
Extended equipment life cycles by 18%, saving millions in deferred capex.
Achieved 15% improvement in overall production efficiency through real-time optimization.
Reduced methane leaks and improved compliance by using AI-powered drone inspections and infrared imaging.
Broader Transformation
The success in the Permian Basin led to enterprise-wide rollout by 2024:
AI is now integrated into cargo route optimization, refining processes, and supply chain demand forecasting.
Chevron reports an estimated $900 million in operational savings over 3 years, directly attributed to AI initiatives.
“AI allows us to make better decisions, faster—turning billions of data points into actionable insights that protect people, the planet, and profitability.” — Jay Johnson, EVP, Upstream, Chevron
Lessons for the Industry
Chevron’s journey underscores that:
AI is not a plug-and-play solution; it requires robust data infrastructure, skilled talent, and change management.
The highest ROI is seen when AI is aligned with core operational KPIs like uptime, safety, and yield.
Partnerships with tech vendors and open data ecosystems accelerate innovation.
AI is Reshaping the Future of Oil & Gas—Are You In? Join the wave of innovation transforming the energy industry.
Artificial Intelligence in Oil and Gas Market Opportunity:
What is the Opportunity for Artificial Intelligence (AI) in Oil and Gas Market?
Supply Chain Management Unlocks Opportunity
The increasing focus on transforming the supply chain management of the oil & gas sector increases the adoption of AI. It provides insights into logistic planning, demand forecasting, and inventory management that help in optimizing supply chain operations. The focus on better customer satisfaction, improving efficiency, and reducing costs fuels demand for AI.
The need for identifying disruption in the supply chain increases the adoption of AI’s predictive analysis. It analyses market trends, past data, and weather patterns, and helps in avoiding shortages. It helps in supply chain management from upstream operations to distribution, which supports market growth.
The growing need for resource allocation, optimization of transportation routes, and scheduling requires AI. The increasing focus on real-time visibility of supply chains fuels demand for AI. Supply chain management creates an opportunity for the growth of the artificial intelligence oil & gas market.
Artificial Intelligence (AI) in Oil and Gas Market Challenges and Limitations:
What is the Limitation for Artificial Intelligence (AI) in Oil and Gas Market?
High Implementation Cost Limits Adoption of AI in Oil & Gas Market
Despite several benefits of AI in the oil & gas sector, the high implementation cost restricts the market growth. Factors like the requirement of data management, acquisition, & integration, need for specialized expertise, cybersecurity challenges, and integration with systems require high implementation cost. The analysis, collection, and storage of data requires a data management & infrastructure system that increases the implementation cost. The need for expertise in software development, data science, and machine learning leads to higher implementation costs.
The integration with legacy systems requires modifications and upgrades, which increases the cost. The risk of cybersecurity requires monitoring and robust security measures, leading to higher implementation costs. The need for high costs for system integration, hardware, and software directly affects the market. The high implementation cost hampers the artificial intelligence (AI) in the oil & gas market.
AI in Oil and Gas Market Coverage:
Report Attributes
Key Statistics
Market Size in 2024
USD 6.69 Billion
Market Size in 2025
USD 7.64 Billion
Market Size in 2030
USD 14.84 Billion
Market Size in 2032
USD 19.35 Billion
Market Size by 2034
USD 25.24 Billion
Growth Rate (2025 to 2034)
CAGR of 14.2%
Leading Region in 2024
North America (captured 39.13% of market share)
Base Year
2024
Forecast Period
2025 to 2034
Segments Covered
Component, Function, Application and Region
Regions Covered
North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa
Key Players
Microsoft Corporation, FuGenX Technologies Pvt. Ltd, IBM Corporation, C3.AI, Google LLC, NVIDIA Corp., Royal Dutch Shell PLC, PJSC Gazprom Neft, Huawei Technologies Co. Ltd, Intel Corporation, Neudax, Infosys Limited, and Others.
Artificial Intelligence in Oil and Gas Market Key Regional Analysis:
Which Region Dominated the Artificial Intelligence (AI) in Oil & Gas Market?
North America dominated the Artificial Intelligence (AI) in Oil & Gas Market in 2024. The well-established infrastructure for the extraction of oil & gas increases demand for AI solutions. The growing private companies and government investment in research & development in the oil & gas sector for the implementation of AI help the market growth. The growing production of oil & gas increases demand for AI solutions. The focus on enhancing operational efficiency and optimizing production and exploration in the oil & gas sector fuels demand for AI, driving the overall growth of the market.
U.S. AI in Oil and Gas Market Size and Forecast 2025 to 2034
How Big is the U.S. AI in Oil and Gas Market?
According to Precedence Research, the U.S. artificial intelligence (AI) in oil and gas market size was valued at USD 1.84 billion in 2024 and is estimated to grow from USD 2.12 billion in 2025 to USD 7.34 billion by 2034. The market is poised to grow at a CAGR of 14.8% from 2025 to 2034.
Why U.S. Stands Out:
Dominated by innovation and early AI adoption across upstream, midstream, and downstream operations.
Proven applications include predictive maintenance, real-time drilling analytics, reservoir modeling, and operational automation.
Leading contributions from tech-forward energy companies such as Chevron, ExxonMobil, SLB, and early rollout of platforms like IBM Watsonx in 2024.
Adoption and Industry Impact in the U.S.
The oil industry is allocating approximately USD 3.1 billion in AI spending in 2024, representing under 5% of total capital expenditures. AI investment is projected to increase up to 80% within five years. (Source: https://www.barrons.com)
At CERAWeek 2025, U.S. firms like BP, Devon Energy, and Chevron showcased improved productivity thanks to AI: Devon reported a 25% increase in well lifespan, while Chevron uses AI‑driven drones for maintenance and monitoring. (Source: https://www.reuters.com)
Why is Asia Pacific Growing in theArtificial Intelligence (AI) in Oil & Gas Market?
Asia Pacific is experiencing the fastest growth in the market during the forecast period. The increasing production activities and exploration in the oil & gas industry increase demand for AI solutions. The focus on digital transformation in the oil & gas sector fuels the adoption of AI solutions to improve operational efficiency. The increasing demand for cost savings and productivity gains helps in the market growth.
The demand for reducing downtime, potential equipment failures, and proactive maintenance increases the adoption of AI solutions. The increasing optimization of operations like refining, drilling, and production increases demand for AI solutions. The growing adoption of AI across applications like downstream, upstream, and midstream drives the overall growth of the market.
Artificial Intelligence (AI) in Oil and Gas Market Segmentation Analysis
Component Analysis:
Why did Software Dominate the Artificial Intelligence (AI) in Oil & Gas Market?
The software segment dominated the Artificial Intelligence (AI) in oil & gas market in 2024. The presence of large amounts of data through diverse sources like seismic surveys, sensors, and drilling operations increases demand for software to identify correlations, patterns, and anomalies. The need for automating repetitive tasks like equipment monitoring, data entry, and report generation in the oil & gas sector increases demand for AI software.
The focus on extending critical equipment lifespans, preventing unexpected downtime, and reducing maintenance costs requires AI software. The growing demand for software for applications like ensuring compliance, monitoring worker safety, and detecting potential hazards drives the market growth.
Function Analysis:
How Predictive Maintenance Segment Held the Largest Share in the Artificial Intelligence (AI) in Oil & Gas Market?
The predictive maintenance segment held the largest revenue share in the Artificial Intelligence (AI) in oil & gas market in 2024. The focus on minimizing unplanned downtime in the oil & gas sector increases demand for predictive maintenance. The increasing demand for extending the lifespan of equipment and identifying issues of equipment early increases the adoption of predictive maintenance.
The focus on minimizing disruptions during planned downtime requires predictive maintenance. The increasing demand for predictive maintenance for optimizing maintenance schedules and enhancing operational efficiency in the oil & gas sector supports the overall growth of the market.
Application Analysis:
How Application Segment Dominates Artificial Intelligence (AI) in Oil & Gas Market?
The upstream segment dominated the Artificial Intelligence (AI) in oil & gas market in 2024. The growing demand for predictive equipment failures, improving operational efficiency, optimizing drilling, and reducing downtime in upstream applications increases demand for AI. The growing focus on extracting and finding oil & gas fuels adoption of AI. The growing utilization of AI functionalities like predictive maintenance in the upstream applications supports the market growth.
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Artificial Intelligence (AI) in Oil and Gas Market Top Companies
Recent Developments
In September 2024, Huawei launched an AI application for oil & gas upstream. Innovation aims to increase production & reserves, achieve high-quality development, enhance industrial quality with intelligence, and ensure safe operations. The focus is on enhancing security, reducing cost, and boosting operational efficiency. (Source: https://e.huawei.com)
In September 2024, APA Corporation collaborated with Palantir to use AI technology for oil & gas operations. The Palantir focuses on the development of software for supply chain management, production optimization, operational planning, maintenance planning, and contract management. The company’s AI solution helps in optimizing raw material logistics, improving equipment reliability, and AIP in invoice & contract documents. (Source: https://www.businesswire.com)
In July 2024, Indosat launched AI solutions for the upstream oil & gas industry. The solution is cloud-based and helps in predictive maintenance for equipment and condition-based monitoring. The solution is used to improve collaborations among workers and monitor workplace safety. (Source: https://developingtelecoms.com/)
Artificial Intelligence (AI) in Oil and Gas Market Segments Covered in the Report
By Component
Software
Hardware
Services
By Function
Predictive Maintenance
Machinery Inspection
Material Movement
Production Planning
Field Services
Quality Control
Reclamation
By Application
Upstream
Midstream
Downstream
By Region
North America
Europe
Germany
UK
France
Italy
Spain
Sweden
Denmark
Norway
Asia Pacific
China
Japan
India
South Korea
Thailand
Latin America
Middle East & Africa
South Africa
UAE
Saudi Arabia
Kuwait
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Japanese Bitcoin treasury Metaplanet Inc. secured shareholder approval for a proposal enabling it to raise as much as ¥555 billion ($3.8 billion) via preferred shares, in a bid to expand its financing options after its stock slumped.
Bottom Line: Mount Sinai researchers developed an AI model to make individualized treatment recommendations for atrial fibrillation (AF) patients-helping clinicians accurately decide whether or not to treat them with anticoagulants (blood thinner medications) to prevent stroke, which is currently the standard treatment course in this patient population. This model presents a completely new approach for how clinical decisions are made for AF patients and could represent a potential paradigm shift in this area.
In this study, the AI model recommended against anticoagulant treatment for up to half of the AF patients who otherwise would have received it based on standard-of-care tools. This could have profound ramifications for global health.
Why the study is important: AF is the most common abnormal heart rhythm, impacting roughly 59 million people globally. During AF, the top chambers of the heart quiver, which allows blood to become stagnant and form clots. These clots can then dislodge and go to the brain, causing a stroke.Blood thinners are the standard treatment for this patient population to prevent clotting and stroke; however, in some cases this medication can lead to major bleeding events.
This AI model uses the patient’s whole electronic health record to recommend an individualized treatment recommendation. It weighs the risk of having a stroke against the risk of major bleeding (whether this would occur organically or as a result of treatment with the blood thinner). This approach to clinical decision-making is truly individualized compared to current practice, where clinicians use risk scores/tools that provide estimates of risk on average over the studied patient population, not for individual patients. Thus, this model provides a patient-level estimate of risk, which it then uses to make an individualized recommendation taking into account the benefits and risks of treatment for that person.
The study could revolutionize the approach clinicians take to treat a very common disease to minimize stroke and bleeding events. It also reflects a potential paradigm change for how clinical decisions are made.
Why this study is unique: This is the first-known individualized AI model designed to make clinical decisions for AF patients using underlying risk estimates for the specific patient based on all of theiractual clinical features. It computes an inclusive net-benefit recommendation to mitigate stroke and bleeding.
How the research was conducted: Researchers trained the AI model on electronic health records of 1.8 million patients over 21 million doctor visits, 82 million notes, and 1.2 billion data points. They generated a net-benefit recommendation on whether or not to treat the patient with blood thinners.
To validate the model, researchers tested the model’s performance among 38,642 patients with atrial fibrillation within the Mount Sinai Health System. They also externally validated the model on 12,817 patients from publicly available datasets from Stanford.
Results: The model generated treatment recommendations that aligned with mitigating stroke and bleeding. It reclassified around half of the AF patients to not receive anticoagulation. These patients would have received anticoagulants under current treatment guidelines.
What this study means for patients and clinicians: This study represents a new era in caring for patients. When it comes to treating AF patients, this study will allow for more personalized, tailored treatment plans.
Quotes:
“This study represents a profound modernization of how we manage anticoagulation for patients with atrial fibrillation and may change the paradigm of how clinical decisions are made,” says corresponding author Joshua Lampert, MD, Director of Machine Learning at Mount Sinai Fuster Heart Hospital. “This approach overcomes the need for clinicians to extrapolate population-level statistics to individuals while assessing the net benefit to the individual patient-which is at the core of what we hope to accomplish as clinicians. The model can not only compute initial recommendations, but also dynamically update recommendations based on the patient’s entire electronic health record prior to an appointment. Notably, these recommendations can be decomposed into probabilities for stroke and major bleeding, which relieves the clinician of the cognitive burden of weighing between stroke and bleeding risks not tailored to an individual patient, avoids human labor needed for additional data gathering, and provides discrete relatable risk profiles to help counsel patients.”
“This work illustrates how advanced AI models can synthesize billions of data points across the electronic health record to generate personalized treatment recommendations. By moving beyond the ‘one size fits none’ population-based risk scores, we can now provide clinicians with individual patient-specific probabilities of stroke and bleeding, enabling shared decision making and precision anticoagulation strategies that represent a true paradigm shift,”adds co-corresponding author Girish Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai.
“Avoiding stroke is the single most important goal in the management of patients with atrial fibrillation, a heart rhythm disorder that is estimated to affect 1 in 3 adults sometime in their life”, says co-senior author, Vivek Reddy MD, Director ofCardiac Electrophysiology at the Mount Sinai Fuster Heart Hospital. “If future randomized clinical trials demonstrate that this Ai Model is even only a fraction as effective in discriminating the high vs low risk patients as observed in our study, the Model would have a profound effect on patient care and outcomes.”
“When patients get test results or a treatment recommendation, they might ask, ‘What does this mean for me specifically?’ We created a new way to answer that question. Our system looks at your complete medical history and calculates your risk for serious problems like stroke and major bleedingprior to your medical appointment. Instead of just telling you what might happen, we show you both what and how likely it is to happen to you personally. This gives both you and your doctor a clearer picture of your individual situation, not just general statistics that may miss important individual factors,” says co-first author Justin Kauffman, Data Scientiest with the Windreich Department of Artificial Intelligence and Human Health.
( September 2, 2025, 02:31 GMT | Official Statement) — MLex Summary: South Korea’s National Police Agency, together with the Ministry of the Interior and Safety, plans to set up a fund to nurture domestic companies specializing in the public safety and anti-disaster safety industries. It intends to launch a 20 billion won (about $14.4 million) fund in 2026 — financed equally by the government and private-sector investors — and expand its scale in the future, as demand for public safety technologies continues to grow both at home and abroad in line with increasing application of artificial intelligence in the industry.
The statement, in Korean, is attached….
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