ARTIFICIAL INTELLIGENCE (AI) IN OIL AND GAS MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2025-2032)

Artificial Intelligence (AI) in Oil and Gas Market Size and Forecast

The Artificial Intelligence (AI) in Oil and Gas Market size was valued at USD 3.01 Bn in 2025 and is expected to reach USD 6.92 Bn by 2032, growing at a compound annual growth rate (CAGR) of 12.7% from 2025 to 2032.

Key Takeaways

• By Application, Energy storage system acquires the prominent market share of 53.6% in 2025 owing to its integration of renewable energy to reduce carbon footprint.
• By Component, Renewable energy hold the largest market share in 2025 owing to its cost reduction and operational efficiency.
• By Product, Distributors dominates the overall market share in 2025 owing to its expansion of ai software and hardware ecosystems.
• By Region, North America hold the largest market share of 38.20% in 2025 owing to its high digital maturity and infrastructure readiness.

Market Overview

There are various types of AI products that are helping companies optimize operations and discovery of new reserves. One of the most common types is machine learning and neural network-based algorithms. These algorithms can analyze vast amounts of data from sensors, satellites, seismic images and more to identify patterns and make predictions. They are helping with tasks like predictive maintenance of equipment, enhanced oil recovery from existing fields, and improving drilling operations with more precise steering of drill bits.

Current Events and their Impact on the Artificial Intelligence (AI) in Oil and Gas Market

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End-user Feedback and Unmet Needs in the Artificial Intelligence (AI) in Oil and Gas Market

End-user Feedback and Unmet Needs

• Need for Customizable and Scalable AI Solutions: End-users often report that many AI tools lack flexibility to adapt to diverse operational environments. They seek scalable solutions tailored to specific upstream, midstream, or downstream needs, with seamless integration into legacy systems—highlighting a demand for more configurable, modular AI platforms.
• Shortage of Skilled Workforce for AI Deployment: Oil and gas companies face challenges in adopting AI due to a limited pool of skilled professionals who understand both AI and industry operations. Users call for better training programs and cross-domain expertise to bridge the gap between data science and petroleum engineering.
• Concerns Over Data Security and Infrastructure Readiness: End-users’ express concerns about data privacy, cybersecurity, and inadequate digital infrastructure, especially in remote sites. Many companies need improved cloud capabilities, secure networks, and reliable connectivity before fully leveraging AI-driven automation and analytics in sensitive or offshore operations.

Segmental Insights

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 Artificial Intelligence (AI) in Oil and Gas Market Insights, By Application

Energy Storage System contribute the highest share of the market owing to its rising digitalization and electrification in oilfield equipment.

Energy storage system acquires the prominent market share of 53.6% in 2025. The oil and gas industry's growing reliance on AI technologies drives the demand for energy storage systems. These systems ensure a consistent power supply for AI tools used in predictive maintenance, remote operations, and real-time monitoring. They support edge computing and enable continuous data processing in offshore or off-grid environments. As companies adopt renewable energy sources to reduce emissions, they rely on energy storage to balance supply and demand, boost operational efficiency, and maintain a stable digital infrastructure for AI applications. For instance, in March 2025, Zendure, a rapidly expanding EnergyTech company, launched two groundbreaking AI-powered energy storage systems: the SolarFlow 800 Pro—the industry’s first intelligent balcony power plant solution—and the SolarFlow 2400 AC, a lightweight AC-coupled system designed for rooftop photovoltaic homes.

Artificial Intelligence (AI) in Oil and Gas Market Insights, By Component

Renewable Energy contribute the highest share of the market owing to its energy resilience and reduced dependency on fossil fuels.

The oil and gas industry adopts renewable energy to lower emissions and enhance sustainability. Artificial intelligence (AI) drives this transition by optimizing solar, wind, and other renewable sources through real-time forecasting, load management, and energy efficiency analysis. Companies use AI to seamlessly integrate renewables with storage systems in remote or offshore operations, ensuring a continuous power supply. This approach helps them reduce operational costs, comply with environmental regulations, and actively support the energy transition within their existing infrastructure. For instance, in January 2025, SLB, a global energy technology company, and Star Energy Geothermal, a subsidiary of Indonesia’s largest renewable energy firm Barito Renewables, announced a collaboration agreement to accelerate the deployment of advanced technologies for geothermal asset development. Such collaborations are anticipated to boost the AI in oil and gas market revenue.

Artificial Intelligence (AI) in Oil and Gas Market Insights, By Product

Distributors contribute the highest share of the market owing to its rising demand for end-to-end deployment services.

Distributors play a key role in expanding the use of AI technology in the oil and gas industry. They connect AI solution providers with end users by delivering localized support, technical expertise, and specialized implementation services. As demand for AI-driven tools in exploration, drilling, and production grows, distributors help integrate these technologies into existing systems. Their deep understanding of regional markets, procurement practices, and regulatory requirements enables companies to adopt and implement AI solutions more quickly across various segments of the industry.

 For instance, in June 2025, Newtide launched an AI agent for convenience stores and fuel through its platform, Retailers can now create AI agents to support various aspects of their operations, including category management, price optimization, and shift scheduling. The only limitations are the data and systems the agents can access.

Regional Insights

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North America Artificial Intelligence (AI) in Oil and Gas Market Trends

North America dominates the AI in oil and gas market with a share of 38.20%, with extensive adoption across upstream, midstream, and downstream operations. U.S. operators increasingly implement AI-powered technologies—such as digital twins, predictive analytics, and autonomous drilling—to enhance exploration, boost production, and improve safety. The Permian Basin in Texas exemplifies this trend by using AI to streamline drilling and accelerate reservoir analysis. Strong infrastructure, robust R&D efforts, and heavy investment from major energy companies continue to drive rapid AI integration across the region.

Asia Pacific Artificial Intelligence (AI) in Oil and Gas Market Trends

Asia-Pacific businesses are increasingly adopting AI-driven digital oilfield technologies like IoT, cloud, and edge computing to automate monitoring, predictive maintenance, and reservoir optimization. This enhances operational efficiency and reduces costs for both onshore and offshore operations. Rapid industrialization and growing energy demand in China, India, and Southeast Asia drive AI implementation across upstream, midstream, and downstream activities. Both state-owned and private operators actively use AI to boost exploration accuracy, improve production rates, and strengthen safety protocols across their operations.

In April 2023, Huawei introduced an E&P solution along with an intelligent architecture for the oil and gas industry. The architecture connects seamlessly with both new and existing third-party platforms and data lakes, and it supports widely used third-party frameworks.

India Artificial Intelligence (AI) in Oil and Gas Market Trends

In India, companies are leveraging AI-driven solutions to streamline drilling planning, interpret seismic data, and model reservoirs more effectively. Beyond upstream activities, they apply AI to enhance refining efficiency, optimize logistics, and manage material flow, enabling sustainable, data-driven decisions across operations. Indian oil and gas firms are actively investing in AI, IoT, and analytics to modernize their infrastructure. They implement AI-powered predictive maintenance across rigs and refineries to monitor equipment health, predict failures, reduce downtime, and lower operational costs while improving overall asset reliability.

For instance, in June 2025, Cairn Oil & Gas, a subsidiary of Vedanta Limited and India’s largest private oil and gas exploration and production company, launched CAIRA (Cairn Artificial Intelligence and Research Assistant), its proprietary GenAI-based platform, to improve business operations and boost efficiency.

United States Artificial Intelligence (AI) in Oil and Gas Market Trends

American companies and investors are increasingly funding AI-based energy analytics platforms like Novi Labs to enable smarter capital deployment across oil, gas, and data center energy needs. U.S. operators are actively adopting AI tools such as digital twins, machine learning for drill steering, and predictive maintenance to accelerate drilling and reduce costs. During the CERAWeek conference, companies like Devon, Chevron, and BP shared that these technologies have led to improved asset reliability and significantly faster, more cost-effective production.

For instance, in April 2025, U.S. technology company Honeywell introduced Honeywell Protonium, a suite of AI-enabled and ML-powered technologies designed to enhance the efficiency, cost-effectiveness, and scalability of green hydrogen production.

Market Report Scope

Artificial Intelligence (AI) in Oil and Gas Market Trend

Digital transformation efforts by oil and gas companies

The oil and gas sector has been pushing towards increased digitalization of their operations to drive productivity and efficiency gains. There is a growing realization that artificial intelligence can be leveraged to analyze vast amounts of operational data available from rigs, pipelines, refineries, and other assets. This data holds valuable insights that can help predict equipment failures, detect anomalies, and optimize maintenance schedules. AI is also being used for tasks like interpreting seismic data faster to explore new oil and gas reserves. Companies are investing in AI systems that can augment the decision-making capabilities of human workers by providing real-time recommendations.

Need for improved safety and risk mitigation

Another key growth driver is the need to improve safety and mitigate risks in oil and gas operations. The industry deals with hazardous materials and large equipment in complex environments. Even small accidents can have catastrophic consequences both for the environment and human lives. AI is bringing more predictive analytics capabilities that use past incident data to foresee anomalies and anomalies. This enables companies to take preemptive actions to avoid failures and hazardous situations.

Artificial Intelligence (AI) in Oil and Gas Market Opportunity

Increased adoption of AI for predictive maintenance

Increased adoption of AI for predictive maintenance presents a huge opportunity for the oil and gas industry. Predictive maintenance by using AI aims to monitor equipment performance and predict failures in advance. This helps minimize downtime and unplanned outages of critical assets. The technology analyzes vast amounts of operational data such as vibrations, temperatures, pressures, and others collected from sensors by using machine learning models. It can detect subtle changes in equipment behavior indicating impending flaws. This allows preemptive or conditional maintenance to be scheduled at optimal times to avoid unexpected breakdowns.

Blood Flow Measurement Devices Market News

• In January 2025, Italian oil and gas giant Eni S.p.A launched its next-generation supercomputer to enhance its oil and gas exploration capabilities and advance its decarbonization strategies.
• In January 2025, Accenture launched AI Refinery™ for Industry, offering a suite of 12 industry agent solutions to help organizations quickly develop and deploy AI agents that enhance their workforce, tackle industry-specific challenges, and accelerate business value creation.
• In November 2024, At ADIPEC, ADNOC and AIQ announced the launch of ENERGYai, the world’s first custom-built agentic artificial intelligence (AI) solution designed for global energy transformation. ENERGYai will integrate large language model technology with advanced agentic AI—featuring specialized AI agents trained to perform specific tasks across ADNOC’s value chain.
• In May 2024, eDrilling introduced AI software two decades ago to prevent incidents and reduce non-productive time. Now, the company is advancing this effort with the AI Drilling Agent—a digital companion designed to support drillers, drilling engineers, and crews in oil and gas operations and beyond.

Analyst Opinion (Expert Opinion)

• The oil and gas industry are no longer asking if AI can deliver value—but rather how fast and where it can be deployed at scale. In my view, AI adoption is evolving from isolated pilots to mission-critical infrastructure, especially in upstream operations, where real-time data and decision-making can make or break profit margins. For instance, BP reported a 20% improvement in drilling speed in certain wells after deploying AI-driven predictive models and digital twins—demonstrating that AI is not just a tech layer, but a production enabler.
• Despite skepticism around ROI, the results speak volumes. ExxonMobil has leveraged AI to cut seismic data interpretation time from weeks to hours, enabling faster field development. The industry’s complex data environment—ranging from subsurface geology to equipment telemetry—is a perfect fit for machine learning models that can process nonlinear, multivariate patterns better than any human or legacy software. Operators that fail to integrate AI into their asset management workflows risk falling behind not just technologically, but economically.
• Furthermore, AI’s role in decarbonization is drastically undervalued. AI-based optimization in flare gas recovery, pipeline leak detection, and energy efficiency can yield significant environmental gains. Shell’s use of AI for energy optimization at refineries has reportedly led to a 10% reduction in energy consumption in certain facilities—an operational win with climate implications.
• In conclusion, the AI winners in oil and gas will not be those with the most tools, but those with the best integration strategy—grounded in domain expertise, data readiness, and a vision beyond experimentation.

Market Segmentation

• Artificial Intelligence (AI) in Oil and Gas Market, By Application
  • Energy Storage Systems (ESS)
  • Industrial Applications
• Artificial Intelligence (AI) in Oil and Gas Market, By Component         
  • Renewable Energy
  • Utilities
  • Manufacturing
• Artificial Intelligence (AI) in Oil and Gas Market, By Product
  • Direct Sale
  • Distributors
• Artificial Intelligence (AI) in Oil and Gas Market, By Region
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  • Europe
    • Germany
    • U.K.
    • Spain
    • France
    • Italy
    • Russia
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • ASEAN
    • Rest of Asia Pacific
  • Middle East
    • GCC Countries
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • North Africa
    • Central Africa

Sources

Primary Research interviews

• Interviews with AI solution providers for oil & gas applications (e.g., data scientists, product managers).
• Conversations with digital transformation heads at companies like Shell, BP, and ONGC.
• Field engineers and plant managers in upstream and downstream facilities using AI tools.

Databases

• IEEE Xplore Digital Library
• ScienceDirect
• U.S. Energy Information Administration (EIA)
• Society of Petroleum Engineers (SPE) Technical Library

Magazines

• Oil & Gas Journal
• Offshore Engineer Magazine
• World Oil
• Journal of Petroleum Technology (JPT)

Journals’

• Journal of Petroleum Science and Engineering
• Energy AI (Elsevier)
• SPE Reservoir Evaluation & Engineering
• Journal of Natural Gas Science and Engineering

Newspapers

• The Wall Street Journal (Energy Section)
• The Economic Times (Energy Section)
• Financial Times (Commodities & Energy)
• Reuters (Energy & Commodities News)

Associations

• Society of Petroleum Engineers (SPE)
• American Petroleum Institute (API)
• International Association of Oil & Gas Producers (IOGP)
• International Energy Agency (IEA)

Public Domain sources

• U.S. Department of Energy (DOE)
• Oil and Natural Gas Corporation (ONGC) annual reports
• BP Statistical Review of World Energy
• Official press releases from companies like Chevron, Schlumberger, and Halliburton
• National Oil Company (NOC) digital strategy whitepapers

Proprietary Elements

• CMI Data Analytics Tool, and Proprietary CMI Existing Repository of information for last 8 years