석유 및 가스용 AI 및 ML 시장 기회, 성장 촉진요인, 산업 동향 분석, 예측(2025-2034년)

The Global AI And ML In Oil And Gas Market was valued at USD 2.5 billion in 2024 and is projected to grow at a CAGR of 7.1% from 2025 to 2034. This growth is largely driven by the increasing focus on digital transformation, the need for enhanced operational efficiency, and the rising importance of predictive analytics. Companies are leveraging artificial intelligence and machine learning technologies to optimize processes, reduce costs, and improve decision-making across various operations in the industry.

The adoption of AI and ML is reshaping the oil and gas sector, delivering significant benefits in efficiency, cost management, and safety compliance. These technologies play a critical role in ensuring better operational performance, heightened safety standards, and stronger environmental compliance, making them indispensable for modern oil and gas operations.

The market is further bolstered by the growing demand for advanced analytics, which is essential for extracting actionable insights from large datasets, including geospatial, seismic, and operational information. By utilizing these insights, companies can enhance exploration, drilling, production, and maintenance activities, resulting in improved decision-making and minimized downtime. The oil and gas analytics market is also on the rise, with expected revenue of approximately USD 33 billion by 2030, reflecting an annual growth rate of over 20%.

In terms of operations, the market is segmented into upstream, midstream, and downstream activities. The upstream segment, valued at over USD 1 billion in 2024, is experiencing rapid growth due to the integration of AI-driven solutions for seismic interpretation, well planning, and production optimization. These advancements enable real-time decision-making, leading to higher efficiency and reduced downtime in exploration and production processes.

The application scope of AI and ML in the oil and gas industry spans areas such as exploration and production (E&P) optimization, reservoir management, drilling optimization, asset monitoring, pipeline monitoring, and supply chain optimization. The E&P optimization segment is anticipated to grow at a CAGR of over 5% during the forecast period, driven by AI-powered technologies that improve resource identification, enhance performance, and lower operational risks.

Regionally, North America dominated the market in 2024, accounting for 30% of the revenue share. The region's well-developed energy sector and substantial investments in digital solutions have accelerated the adoption of AI technologies, particularly in predictive maintenance, drilling optimization, and reservoir management, all of which prioritize cost efficiency and sustainability.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates & calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimation
• 1.3 Forecast model
• 1.4 Primary research and validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market scope & definition

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2034

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Technology providers
  • 3.2.2 Platform providers
  • 3.2.3 Oil & gas operators
  • 3.2.4 Distributors
  • 3.2.5 End users
• 3.3 Profit margin analysis
• 3.4 Technology & innovation landscape
• 3.5 Patent analysis
• 3.6 Regulatory landscape
• 3.7 Used cases
  • 3.7.1 Used case 1
    • 3.7.1.1 Benefits
    • 3.7.1.2 ROI
  • 3.7.2 Used case 2
    • 3.7.2.1 Benefits
    • 3.7.2.2 ROI
• 3.8 Case study
  • 3.8.1 Case study 1
    • 3.8.1.1 Consumer name
    • 3.8.1.2 Challenge
    • 3.8.1.3 Solution
    • 3.8.1.4 Impact
  • 3.8.2 Case study 2
    • 3.8.2.1 Consumer name
    • 3.8.2.2 Challenge
    • 3.8.2.3 Solution
    • 3.8.2.4 Impact
• 3.9 Impact forces
  • 3.9.1 Growth drivers
    • 3.9.1.1 Rising demand for operational efficiency
    • 3.9.1.2 Growing adoption of predictive maintenance
    • 3.9.1.3 Increasing focus on data-driven decision making
    • 3.9.1.4 Rising investment in digital transformation
  • 3.9.2 Industry pitfalls & challenges
    • 3.9.2.1 Data quality and integration challenges
    • 3.9.2.2 Skilled workforce shortage
• 3.10 Growth potential analysis
• 3.11 Porter's analysis
• 3.12 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Offering, 2021 - 2034 ($Bn)

• 5.1 Key trends
• 5.2 Platform
• 5.3 Service

Chapter 6 Market Estimates & Forecast, By Operation, 2021 - 2034 ($Bn)

• 6.1 Key trends
• 6.2 Upstream
• 6.3 Midstream
• 6.4 Downstream

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2034 ($Bn)

• 7.1 Key trends
• 7.2 Exploration and production (E&P) optimization
• 7.3 Reservoir management
• 7.4 Drilling optimization
• 7.5 Asset monitoring and management
• 7.6 Pipeline monitoring and leak detection
• 7.7 Supply chain optimization
• 7.8 Others

Chapter 8 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)

• 8.1 Key trends
• 8.2 National Oil Companies (NOCs)
• 8.3 Independent Oil Companies (IOCs)

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Nordics
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 Australia
  • 9.4.5 South Korea
  • 9.4.6 Southeast Asia
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 MEA
  • 9.6.1 UAE
  • 9.6.2 South Africa
  • 9.6.3 Saudi Arabia

Chapter 10 Company Profiles

• 10.1 ABB
• 10.2 Ambyint
• 10.3 Aspen Technology
• 10.4 Baker Hughes
• 10.5 C3.ai
• 10.6 Dataiku
• 10.7 Emerson Electric
• 10.8 Halliburton
• 10.9 Honeywell
• 10.10 IBM
• 10.11 Intel
• 10.12 Microsoft
• 10.13 Palantir
• 10.14 Petro.ai
• 10.15 Rockwell Automation
• 10.16 Schlumberger
• 10.17 Siemens Energy
• 10.18 SparkCognition
• 10.19 Weatherford
• 10.20 Yokogawa Electric