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AI in Oil and Gas Market by Technology (Machine Learning, Natural Language Processing, Robotics Process Automation, Predictive Analytics), by Deployment Mode (On-Premises, Cloud-Based, Hybrid Deployment), by Application (Exploration & Drilling, Production Optimization, Reservoir Management, Equipment Maintenance & Monitoring, Supply Chain & Logistics, Health, Safety & Environment (HSE) Management), by End-User (Oil & Gas Exploration Companies, Oil & Gas Production Companies, Oilfield Service Providers, Refining Companies, Engineering & Construction Firms), and by Region; Global Insights & Forecast (2024 – 2030) and By Region; Global Insights & Forecast (2024 – 2030)

Published: December, 2024  
|   Report ID: EP4675  
|   Energy and Power

As per Intent Market Research, the AI in Oil and Gas Market was valued at USD 4.0 billion in 2023 and will surpass USD 12.1 billion by 2030; growing at a CAGR of 17.2% during 2024 - 2030.

The integration of artificial intelligence (AI) in the oil and gas sector has been transforming operational workflows, driving efficiency, and enhancing productivity. The industry’s increasing reliance on AI solutions is primarily fueled by the growing need for cost reduction, automation, and predictive capabilities. From exploration to production, AI technologies such as machine learning, natural language processing (NLP), and predictive analytics are helping organizations optimize their processes, improve safety standards, and ensure sustainability. The AI in oil and gas market is expected to grow significantly, propelled by advancements in digital technologies and a shift towards more data-driven decision-making.

Machine Learning Segment is Largest Owing to Operational Efficiency Gains

Machine learning (ML) is the dominant AI technology in the oil and gas industry, contributing heavily to operational optimization. This technology’s ability to process vast amounts of data and generate actionable insights is driving its adoption across multiple oil and gas applications. In exploration and drilling, machine learning models are utilized to analyze seismic data, predict reservoir performance, and optimize drilling locations. Furthermore, machine learning algorithms support predictive maintenance by detecting equipment anomalies before failures occur, reducing unplanned downtime, and extending the lifespan of machinery. As a result, machine learning helps oil and gas companies improve operational efficiency and minimize costs.

The largest use of machine learning is in upstream operations, where its ability to predict well production and optimize drilling techniques has significantly reduced operational risks and costs. Machine learning is also making strides in the midstream and downstream sectors, particularly in supply chain optimization and refining processes. As more data becomes available through sensors and IoT devices, the scope for machine learning to create predictive models and enhance decision-making continues to grow, cementing its position as the largest subsegment in the AI in oil and gas market.

AI in Oil and Gas Market Size

Cloud-Based Deployment Mode is Fastest Growing Due to Scalability

Cloud-based deployment is rapidly becoming the preferred solution in the AI-driven oil and gas market. The ability to leverage cloud infrastructure provides organizations with scalable, cost-effective, and flexible solutions to store and process vast amounts of data. Cloud platforms allow companies to deploy AI models, analyze data in real time, and access insights from anywhere, facilitating better decision-making across all stages of the oil and gas value chain. As the oil and gas industry increasingly moves towards digital transformation, the shift from on-premises to cloud-based AI solutions has accelerated.

The growth of cloud-based deployment is particularly noticeable in the downstream and midstream sectors, where companies are seeking to enhance their supply chain management and improve efficiency. Moreover, with the growing number of AI applications for predictive maintenance, reservoir management, and exploration, cloud-based AI solutions are crucial for oil and gas companies to stay competitive. The transition to the cloud is projected to continue its rapid growth, making cloud-based deployment the fastest-growing subsegment in the AI in oil and gas market.

Exploration & Drilling Application is Largest Due to Technological Advancements

Exploration and drilling remain the largest application for AI in the oil and gas market. AI technologies are widely used to optimize drilling techniques, reduce exploration risks, and improve resource detection. Through the application of AI, seismic data can be analyzed more accurately, leading to better insights into reservoir characteristics. Machine learning and deep learning algorithms enable real-time analysis of exploration data, enhancing decision-making regarding the best drilling locations and reducing the chances of dry wells. AI also improves efficiency by automating the drilling process and optimizing drilling parameters in real time.

With advancements in AI-based technologies, exploration and drilling companies are now able to access predictive models that not only improve the accuracy of reservoir predictions but also help in reducing operational costs. AI is expected to continue playing a key role in this area, as companies look for ways to minimize exploration risks and enhance the speed of their drilling activities. As the oil and gas sector moves towards digitalization, exploration and drilling will remain the largest application segment for AI.

Oil & Gas Exploration Companies are Key End-Users of AI Solutions

Oil and gas exploration companies are the largest end-users of AI technology, as these companies deal with the complexities of resource discovery, reservoir management, and geological data analysis. AI technologies, such as machine learning and predictive analytics, allow exploration companies to significantly improve their ability to predict the location and volume of oil and gas reserves. Furthermore, AI helps in optimizing drilling operations by analyzing data in real-time, reducing the number of unsuccessful drills, and improving the efficiency of exploration processes.

The increasing need for efficient and cost-effective exploration methods is driving the adoption of AI solutions by exploration companies. With AI enabling better data management, advanced simulations, and faster decision-making, exploration companies are able to reduce operational costs and environmental impact. This growing reliance on AI for exploration and resource management makes oil and gas exploration companies the largest end-users in the market.

North America Leads the Market Due to Technological Advancements

North America dominates the AI in oil and gas market, driven by significant investments in advanced technologies and the presence of major oil and gas companies that are leading the charge in AI adoption. The United States, in particular, is at the forefront of integrating AI solutions within the oil and gas industry, where large-scale AI implementations are enhancing exploration, drilling, and production optimization. The region’s strong infrastructure, technological innovation, and collaboration between technology providers and oil and gas companies are key factors contributing to its dominance in the AI market.

Moreover, North America’s oil and gas companies are leveraging cloud-based AI solutions to improve operational efficiencies, predictive maintenance, and health, safety, and environmental (HSE) management. This technological advancement, combined with regulatory support for digital transformation, is expected to ensure North America remains a dominant region for AI adoption in the oil and gas sector through 2030.

AI in Oil and Gas Market Size by Region 2030

Competitive Landscape and Leading Companies

The AI in oil and gas market is highly competitive, with major technology players and oil and gas companies working together to implement AI solutions. Leading companies in the market include IBM Corporation, Microsoft Corporation, Google LLC, Schlumberger Limited, Halliburton Company, and Baker Hughes, among others. These companies are at the forefront of AI innovation, providing AI-powered platforms, cloud-based solutions, and data analytics tools designed to optimize oil and gas operations.

Strategic partnerships and collaborations are common in this market, as oil and gas companies increasingly work with technology providers to integrate AI solutions into their operations. The competitive landscape is marked by a trend towards mergers and acquisitions, with large companies acquiring AI-focused startups to enhance their technological capabilities. As the market continues to grow, these leading players are expected to maintain their competitive edge through continuous innovation and a focus on AI applications that drive efficiency, cost savings, and safety improvements across the oil and gas value chain.

Recent Developments:

  • Microsoft partnered with Chevron to implement AI solutions for predictive maintenance and operational optimization in the oil and gas industry.
  • Schlumberger launched a new AI-driven platform to improve exploration and production efficiency, integrating real-time data analytics for optimized decision-making.
  • Baker Hughes introduced AI-powered software to enhance reservoir management and optimize production for oilfield operators.
  • Halliburton announced a collaboration with AWS to integrate machine learning and AI technologies into its cloud-based solutions for drilling and production.
  • IBM acquired an AI-based startup focused on predictive analytics to strengthen its capabilities in optimizing oil and gas operations.

List of Leading Companies:

  • Accenture
  • AWS (Amazon Web Services)
  • Baker Hughes
  • Cisco Systems, Inc.
  • Dassault Systèmes
  • GE Digital
  • Google LLC
  • Halliburton Company
  • Honeywell International Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • Rockwell Automation
  • Schlumberger Limited
  • Siemens AG

Report Scope:

Report Features

Description

Market Size (2023)

USD 4.0 Billion

Forecasted Value (2030)

USD 12.1 Billion

CAGR (2024 – 2030)

17.2%

Base Year for Estimation

2023

Historic Year

2022

Forecast Period

2024 – 2030

Report Coverage

Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments

Segments Covered

AI in Oil and Gas Market by Technology (Machine Learning, Natural Language Processing, Robotics Process Automation, Predictive Analytics), by Deployment Mode (On-Premises, Cloud-Based, Hybrid Deployment), by Application (Exploration & Drilling, Production Optimization, Reservoir Management, Equipment Maintenance & Monitoring, Supply Chain & Logistics, Health, Safety & Environment (HSE) Management), by End-User (Oil & Gas Exploration Companies, Oil & Gas Production Companies, Oilfield Service Providers, Refining Companies, Engineering & Construction Firms)

Regional Analysis

North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, South Korea, Australia, India, and Rest of Asia-Pacific), Latin America (Brazil, Argentina, and Rest of Latin America), Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa)

Major Companies

Accenture, AWS (Amazon Web Services), Baker Hughes, Cisco Systems, Inc., Dassault Systèmes, GE Digital, Google LLC, Halliburton Company, Honeywell International Inc., IBM Corporation, Microsoft Corporation, Oracle Corporation, Rockwell Automation, Schlumberger Limited and Siemens AG

Customization Scope

Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements

1. Introduction

   1.1. Market Definition

   1.2. Scope of the Study

   1.3. Research Assumptions

   1.4. Study Limitations

2. Research Methodology

   2.1. Research Approach

      2.1.1. Top-Down Method

      2.1.2. Bottom-Up Method

      2.1.3. Factor Impact Analysis

  2.2. Insights & Data Collection Process

      2.2.1. Secondary Research

      2.2.2. Primary Research

   2.3. Data Mining Process

      2.3.1. Data Analysis

      2.3.2. Data Validation and Revalidation

      2.3.3. Data Triangulation

3. Executive Summary

   3.1. Major Markets & Segments

   3.2. Highest Growing Regions and Respective Countries

   3.3. Impact of Growth Drivers & Inhibitors

   3.4. Regulatory Overview by Country

4. AI in Oil and Gas Market, by Technology (Market Size & Forecast: USD Million, 2022 – 2030)

   4.1. Machine Learning

   4.2. Natural Language Processing (NLP)

   4.3. Robotics Process Automation (RPA)

   4.4. Predictive Analytics

   4.5. Others

5. AI in Oil and Gas Market, by Deployment Mode (Market Size & Forecast: USD Million, 2022 – 2030)

   5.1. On-Premises

   5.2. Cloud-Based

   5.3. Hybrid Deployment

6. AI in Oil and Gas Market, by Application (Market Size & Forecast: USD Million, 2022 – 2030)

   6.1. Exploration & Drilling

   6.2. Production Optimization

   6.3. Reservoir Management

   6.4. Equipment Maintenance & Monitoring

   6.5. Supply Chain & Logistics

   6.6. Health, Safety & Environment (HSE) Management

7. AI in Oil and Gas Market, by End-User (Market Size & Forecast: USD Million, 2022 – 2030)

   7.1. Oil & Gas Exploration Companies

   7.2. Oil & Gas Production Companies

   7.3. Oilfield Service Providers

   7.4. Refining Companies

   7.5. Engineering & Construction Firms

   7.6. Others

8. Regional Analysis (Market Size & Forecast: USD Million, 2022 – 2030)

   8.1. Regional Overview

   8.2. North America

      8.2.1. Regional Trends & Growth Drivers

      8.2.2. Barriers & Challenges

      8.2.3. Opportunities

      8.2.4. Factor Impact Analysis

      8.2.5. Technology Trends

      8.2.6. North America AI in Oil and Gas Market, by Technology

      8.2.7. North America AI in Oil and Gas Market, by Deployment Mode

      8.2.8. North America AI in Oil and Gas Market, by Application

      8.2.9. North America AI in Oil and Gas Market, by End-User

      8.2.10. By Country

         8.2.10.1. US

               8.2.10.1.1. US AI in Oil and Gas Market, by Technology

               8.2.10.1.2. US AI in Oil and Gas Market, by Deployment Mode

               8.2.10.1.3. US AI in Oil and Gas Market, by Application

               8.2.10.1.4. US AI in Oil and Gas Market, by End-User

         8.2.10.2. Canada

         8.2.10.3. Mexico

    *Similar segmentation will be provided for each region and country

   8.3. Europe

   8.4. Asia-Pacific

   8.5. Latin America

   8.6. Middle East & Africa

9. Competitive Landscape

   9.1. Overview of the Key Players

   9.2. Competitive Ecosystem

      9.2.1. Level of Fragmentation

      9.2.2. Market Consolidation

      9.2.3. Product Innovation

   9.3. Company Share Analysis

   9.4. Company Benchmarking Matrix

      9.4.1. Strategic Overview

      9.4.2. Product Innovations

   9.5. Start-up Ecosystem

   9.6. Strategic Competitive Insights/ Customer Imperatives

   9.7. ESG Matrix/ Sustainability Matrix

   9.8. Manufacturing Network

      9.8.1. Locations

      9.8.2. Supply Chain and Logistics

      9.8.3. Product Flexibility/Customization

      9.8.4. Digital Transformation and Connectivity

      9.8.5. Environmental and Regulatory Compliance

   9.9. Technology Readiness Level Matrix

   9.10. Technology Maturity Curve

   9.11. Buying Criteria

10. Company Profiles

   10.1. Accenture

      10.1.1. Company Overview

      10.1.2. Company Financials

      10.1.3. Product/Service Portfolio

      10.1.4. Recent Developments

      10.1.5. IMR Analysis

    *Similar information will be provided for other companies 

   10.2. AWS (Amazon Web Services)

   10.3. Baker Hughes

   10.4. Cisco Systems, Inc.

   10.5. Dassault Systèmes

   10.6. GE Digital

   10.7. Google LLC

   10.8. Halliburton Company

   10.9. Honeywell International Inc.

   10.10. IBM Corporation

   10.11. Microsoft Corporation

   10.12. Oracle Corporation

   10.13. Rockwell Automation

   10.14. Schlumberger Limited

   10.15. Siemens AG

11. Appendix

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A comprehensive market research approach was employed to gather and analyze data on the AI in Oil and Gas Market. In the process, the analysis was also done to analyze the parent market and relevant adjacencies to measure the impact of them on the AI in Oil and Gas Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.

Research Approach - AI in Oil and Gas MarketSecondary Research

Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.

Primary Research

Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the AI in Oil and Gas ecosystem. The primary research objectives included:

  • Validating findings and assumptions derived from secondary research
  • Gathering qualitative and quantitative data on market trends, drivers, and challenges
  • Understanding the demand-side dynamics, encompassing end-users, component manufacturers, facility providers, and service providers
  • Assessing the supply-side landscape, including technological advancements and recent developments

Market Size Assessment

A combination of top-down and bottom-up approaches was utilized to analyze the overall size of the AI in Oil and Gas Market. These methods were also employed to assess the size of various subsegments within the market. The market size assessment methodology encompassed the following steps:

  1. Identification of key industry players and relevant revenues through extensive secondary research
  2. Determination of the industry's supply chain and market size, in terms of value, through primary and secondary research processes
  3. Calculation of percentage shares, splits, and breakdowns using secondary sources and verification through primary sources

Bottom Up and Top Down - AI in Oil and Gas MarketData Triangulation

To ensure the accuracy and reliability of the market size, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size assessment.

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