The Global AI & ML in Oil & Gas Market was valued at USD 767.9 million in 2024 and is projected to grow at a 4.8% CAGR from 2025 to 2034, fueled by rising adoption of artificial intelligence (AI) and machine learning (ML) solutions across upstream operations. The market is driven by the need to enhance operational efficiency, reduce exploration risks, optimize production, and enable predictive maintenance, with significant deployment among National Oil Companies (NOCs) and increasing focus on E&P optimization.
The integration of AI and ML technologies in the oil & gas sector is gaining momentum as companies seek to improve efficiency, reduce costs, and address environmental concerns. AI and ML algorithms help analyze vast datasets from seismic studies, drilling operations, and production workflows to optimize decision-making and reduce operational risks. For instance, machine learning models are being used to predict equipment failures, optimize drilling routes, and enhance reservoir modeling.
Furthermore, the increased focus on reducing non-productive time (NPT) and improving resource management has accelerated the adoption of AI & ML platforms. Technologies such as AI-driven predictive analytics, robotic process automation (RPA), and natural language processing (NLP) are facilitating real-time data analysis and actionable insights for oil & gas operators.
Based on platform (by offering), the AI & ML in oil & gas market is segmented into software, services, and platforms. The platforms segment accounted for a significant market share in 2024 and is projected to reach USD 2.6 billion by 2034. AI/ML platforms enable oil & gas companies to integrate disparate data sources, apply advanced analytics, and automate decision-making processes across operations. These platforms offer customizable and scalable solutions that can be adapted to various upstream activities, from exploration to production optimization.
By operation, AI & ML adoption is predominantly observed in upstream activities, including exploration, drilling, and production. The upstream segment surpassed USD 1.3 billion in 2024 and continues to dominate the market, supported by advancements in data-driven reservoir modeling, predictive drilling analytics, and real-time monitoring systems. AI & ML applications in upstream operations enable companies to analyze geological and geophysical data for improved resource identification and extraction. These technologies assist in minimizing exploration risks and optimizing drilling processes, thereby reducing operational costs and enhancing yield efficiency.
Based on application, the AI & ML in oil & gas market is segmented into E&P optimization, drilling optimization, predictive maintenance, reservoir management, and others. Among these, Exploration and Production (E&P) optimization holds a substantial share and is forecasted to reach USD 841.9 million by 2034. E&P optimization leverages AI/ML tools for seismic data analysis, reservoir modeling, real-time drilling optimization, and production forecasting. AI-driven models enable oil & gas companies to enhance resource identification, improve recovery rates, and streamline field development strategies.
Based on end-use, the AI & ML in oil & gas market is segmented into National Oil Companies (NOCs), Independent Oil Companies (IOCs), and Oilfield Service Companies. The NOCs segment is poised to surpass USD 2.4 billion by 2034, driven by significant investments in AI/ML-based solutions for exploration, production, and environmental management. NOCs are increasingly collaborating with AI technology providers to implement data analytics, AI-based monitoring, and production optimization tools to ensure cost-effective and sustainable operations.
Regionally, North America represents a leading market for AI & ML adoption in oil & gas, projected to exceed USD 1.2 billion by 2034. The region benefits from strong technological infrastructure, advanced R&D capabilities, and the presence of major oil & gas operators.U.S.-based companies are at the forefront of AI/ML-driven innovations in shale oil and gas exploration, production automation, and pipeline monitoring. The region’s focus on operational efficiency, cost reduction, and environmental sustainability is further accelerating AI/ML adoption
- Chapter 1 Research Methodology
- 1.1 Research design
- 1.2 Base estimates and calculations
- 1.2.1 Base year calculation
- 1.2.2 Key trends for market estimates
- 1.2.2.1 Technology adoption rate
- 1.2.2.2 Investment rate
- 1.2.2.3 Regulatory environment assessment
- 1.2.2.4 Market demand for efficiency
- 1.2.2.5 Safety incident assessment
- 1.2.2.6 Sustainability goals
- 1.2.2.7 Competitive landscape
- 1.2.2.8 Economic conditions
- 1.2.2.9 Technological advancements
- 1.2.2.10 Partnership & collaboration
- 1.3 Future investment plan of companies
- 1.4 Forecast model
- 1.5 Primary research & validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources
- 1.6 Market definitions
- Chapter 2 Executive Summary
- 2.1 Industry 360 degree synopsis, 2021-2034
- 2.2 Business trends
- 2.2.1 Total Addressable Market (TAM), 2025-2034
- 2.2.1.1 TAM trends
- 2.3 Regional trends
- 2.4 Offering trends
- 2.5 Operation trends
- 2.6 Application trends
- 2.7 End Use trends
- Chapter 3 Industry Insights
- 3.1.1 Technology providers
- 3.1.2 Platform providers
- 3.1.3 Oil & gas operators
- 3.1.4 Distributors
- 3.1.5 End users
- 3.2 Supplier landscape
- 3.3 Technology & innovation landscape
- 3.3.1 Autonomous operations & robotics
- 3.3.2 AI for energy trading & market forecasting
- 3.3.3 Digital twins & AI-driven simulation
- 3.3.4 AI-enhanced refining & downstream optimization
- 3.4 Patent analysis
- 3.4.1 Key news and initiatives
- 3.4.2 Regulatory Landscape
- 3.4.3 North America
- 3.4.3.1 U.S
- 3.4.3.1.1 Energy Policy Act (EPA) of 2005
- 3.4.3.1.2 Federal Energy Regulatory Commission (FERC) Guidelines
- 3.4.3.1.3 Environmental Protection Agency (EPA) AI Compliance Rules
- 3.4.3.2 Canada
- 3.4.3.2.1 Artificial Intelligence and Data Act (AIDA)
- 3.4.3.2.2 Personal Information Protection and Electronic Documents Act (PIPEDA)
- 3.4.3.2.3 Canada Energy Regulator Act (CERA)
- 3.4.3.3 Mexico
- 3.4.3.3.1 Federal Law on Protection of Personal Data (LFPDPPP)
- 3.4.3.3.2 Digital Transformation Policies (CONACYT & AI National Strategy)
- 3.4.4 Europe
- 3.4.4.1 UK
- 3.4.4.1.1 UK Energy Act 2016
- 3.4.4.1.2 Data Protection Act 2018 (UK GDPR)
- 3.4.4.1.3 AI Regulation White Paper (2023)
- 3.4.4.2 Germany
- 3.4.4.2.1 Artificial Intelligence Act (EU AI Act - Pending Finalization)
- 3.4.4.2.2 Federal Mining Act (Bundesberggesetz - BBergG)
- 3.4.4.2.3 Energy Industry Act (Energiewirtschaftsgesetz - EnWG)
- 3.4.4.3 France
- 3.4.4.3.1 EU AI Act
- 3.4.4.3.2 Energy Code (Code de l'énergie)
- 3.4.4.3.3 CNIL AI Guidelines
- 3.4.4.4 Italy
- 3.4.4.4.1 Italian Legislative Decree No. 231/2001 (Corporate Liability)
- 3.4.4.4.2 National Energy and Climate Plan (NECP) & Legislative Decree 152/2006 (Environmental Law)
- 3.4.4.4.3 Occupational Safety Regulations (Legislative Decree 81/2008)
- 3.4.4.5 Spain
- 3.4.4.5.1 Spain's National AI Strategy (ENIA)
- 3.4.4.5.2 Environmental Impact Assessment Law (Law 21/2013)
- 3.4.4.6 Russia
- 3.4.4.6.1 Federal Law on Artificial Intelligence (2021)
- 3.4.4.6.2 Federal Law No. 152-FZ on Personal Data (2006, Amended)
- 3.4.4.6.3 Federal Law on Industrial Safety of Hazardous Production Facilities (116-FZ, 1997, Amended)
- 3.4.5 Asia Pacific
- 3.4.5.1 China
- 3.4.5.1.1 New Generation Artificial Intelligence Development Plan (2017)
- 3.4.5.1.2 Oil and Natural Gas Industry Development Guidelines
- 3.4.5.1.3 Data Security Law (2021)
- 3.4.5.2 India
- 3.4.5.2.1 Information Technology Act, 2000 (IT Act) & Amendments
- 3.4.5.2.2 Bureau of Indian Standards (BIS) - AI & Industry
- 4.0 Standards
- 3.4.5.2.3 Digital Personal Data Protection Act, 2023 (DPDP Act)
- 3.4.5.3 Japan
- 3.4.5.3.1 Industrial Safety and Health Act (ISHA)
- 3.4.5.3.2 Electricity Business Act (For AI in Energy Management)
- 3.4.5.3.3 Environmental Laws (Air Pollution Control Act & Energy Conservation Act)
- 3.4.5.4 Australia
- 3.4.5.4.1 Artificial Intelligence Ethics Framework
- 3.4.5.4.2 Petroleum and Gas (Production and Safety) Act 2004
- 3.4.5.4.3 National Greenhouse and Energy Reporting (NGER) Act 2007
- 3.4.5.5 South Korea
- 3.4.5.5.1 Personal Information Protection Act (PIPA)
- 3.4.5.5.2 Electric Utility Act & Energy Act
- 3.4.5.5.3 Framework Act on National Informatization
- 3.4.5.6 Southeast Asia
- 3.4.5.6.1 Personal Data Protection Act 2010 (PDPA)
- 3.4.5.6.2 Energy Market Authority (EMA) Guidelines
- 3.4.6 Latin America
- 3.4.6.1 Brazil
- 3.4.6.1.1 Brazilian Artificial Intelligence Legal Framework (Bill No. 21/2020)
- 3.4.6.1.2 National Agency of Petroleum, Natural Gas and Biofuels (ANP) Regulations
- 3.4.6.1.3 Brazilian Internet Law (Marco Civil da Internet, Law No. 12,965/2014)
- 3.4.6.2 Argentina
- 3.4.6.2.1 National Hydrocarbons Law (Law No. 17,319)
- 3.4.6.2.2 Personal Data Protection Law (Law No. 25,326)
- 3.4.7 MEA
- 3.4.7.1 UAE
- 3.4.7.1.1 UAE Artificial Intelligence Strategy 2031
- 3.4.7.1.2 Federal Law No. 14 of 2017 on Industrial Regulations for Oil & Gas
- 3.4.7.1.3 Federal Decree-Law No. 45 of 2021 (UAE Data Protection Law)
- 3.4.7.2 Saudi Arabia
- 3.4.7.2.1 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations
- 3.4.7.2.2 Ministry of Energy Regulations on AI in Oil & Gas
- 3.4.7.2.3 Environmental Regulations (MEWA & GSO Standards)
- 3.4.7.3 South Africa
- 3.4.7.3.1 Mineral and Petroleum Resources Development Act (MPRDA) (2002)
- 3.4.7.3.2 National Energy Act (2008)
- 3.4.7.3.3 Artificial Intelligence Policy Framework (Draft, 2021)
- 3.5 Used cases
- 3.5.1 Used case 1
- 3.5.2 Used case 2
- 3.6 Case study
- 3.6.1 Case study 1
- 3.6.2 Case study 2
- 3.7 Industry impact forces
- 3.7.1 Growth drivers
- 3.7.2 Rising demand for operational efficiency
- 3.7.3 Growing adoption of predictive maintenance
- 3.7.4 Increasing focus on data-driven decision making
- 3.7.5 Rising investment in digital transformation
- 3.7.6 Industry pitfalls and challenges
- 3.7.7 Data quality and integration challenges
- 3.7.8 Skilled workforce shortage
- 3.8 Growth potential analysis
- 3.9 Porter's analysis
- 3.10 PESTEL analysis
- Chapter 4 Competitive Landscape
- 4.1 Introduction
- 4.2 Company market share analysis
- 4.3 Competitive positioning matrix
- 4.4 Strategic outlook matrix
- Chapter 5 AI & ML in Oil & Gas Market, By Offering
- 5.1 Key trends
- 5.2 Platform
- 5.3 Service
- Chapter 6 AI & ML in Oil & Gas Market, By Operation
- 6.1 Key trends
- 6.2 Upstream
- 6.3 Midstream
- 6.4 Downstream
- Chapter 7 AI & ML in Oil & Gas Market, By Application
- 7.1 Key trends
- 7.2 Exploration and Production (E&P) optimization
- 7.3 Reservoir management
- 7.4 Drilling optimization
- 7.5 Asset monitoring & management
- 7.6 Pipeline monitoring and leak detection
- 7.7 Supply chain optimization
- 7.8 Others
- Chapter 8 AI & ML in Oil & Gas Market, By End Use
- 8.1 Key trends
- 8.2 National Oil Companies (NOCs)
- 8.3 Independent Oil Companies (IOCs)
- Chapter 9 AI & ML in Oil & Gas Market, By Region
- 9.1 Key trends
- 9.2 North America
- 9.3 Europe
- 9.4 Asia-Pacific
- 9.5 Latin America
- 9.6 MEA
- Chapter 10 Company Profiles
- 10.1 ABB Ltd.
- 10.1.1 Global Overview
- 10.1.2 Market/Business Overview
- 10.1.3 Financial Data
- 10.1.3.1 Sales Revenue, 2022-2024
- 10.1.4 Product Landscape
- 10.1.5 Strategic Outlook
- 10.1.6 SWOT analysis
- 10.2 Ambyint
- 10.2.1 Global Overview
- 10.2.2 Market/Business Overview
- 10.2.3 Financial Data
- 10.2.4 Product Landscape
- 10.2.5 SWOT Analysis
- 10.3 Aspen Technology
- 10.3.1 Global Overview
- 10.3.2 Market/Business Overview
- 10.3.3 Financial Data
- 10.3.3.1 Sales Revenue, 2022-2024
- 10.3.4 Product Landscape
- 10.3.5 SWOT analysis
- 10.4 Baker Hughes
- 10.4.1 Global Overview
- 10.4.2 Market/Business Overview
- 10.4.3 Financial Data
- 10.4.3.1 Sales Revenue, 2022-2024
- 10.4.4 Product Landscape
- 10.4.5 Strategic Outlook
- 10.4.6 SWOT Analysis
- 10.5 C3.ai, Inc.
- 10.5.1 Global Overview
- 10.5.2 Market/Business Overview
- 10.5.3 Financial Data
- 10.5.3.1 Sales Revenue, 2022-2024
- 10.5.4 Product Landscape
- 10.5.5 SWOT Analysis
- 10.6 Dataiku
- 10.6.1 Global Overview
- 10.6.2 Market/Business Overview
- 10.6.3 Financial Data
- 10.6.4 Product Landscape
- 10.6.5 SWOT Analysis
- 10.7 Emerson Electric
- 10.7.1 Global Overview
- 10.7.2 Market/Business Overview
- 10.7.3 Financial Data
- 10.7.3.1 Sales Revenue, 2022-2024
- 10.7.4 Product Landscape
- 10.7.5 SWOT Analysis
- 10.8 Halliburton
- 10.8.1 Global Overview
- 10.8.2 Market/Business Overview
- 10.8.3 Financial Data
- 10.8.3.1 Sales Revenue, 2022-2024
- 10.8.4 Product Landscape
- 10.8.5 SWOT Analysis
- 10.9 Honeywell International Inc.
- 10.9.1 Global Overview
- 10.9.2 Market/Business Overview
- 10.9.3 Financial Data
- 10.9.3.1 Sales Revenue, 2021-2024
- 10.9.4 Product Landscape
- 10.9.5 Strategic Outlook
- 10.9.6 SWOT Analysis
- 10.10 IBM Corporation
- 10.10.1 Global Overview
- 10.10.2 Market/Business Overview
- 10.10.3 Financial Data
- 10.10.3.1 Sales Revenue, 2022-2024
- 10.10.4 Product Landscape
- 10.10.5 SWOT analysis
- 10.11 Intel Corporation
- 10.11.1 Global Overview
- 10.11.2 Market/Business Overview
- 10.11.3 Financial Data
- 10.11.3.1 Sales Revenue, 2020-2023
- 10.11.4 Product Landscape
- 10.11.5 Strategic Outlook
- 10.11.6 SWOT analysis
- 10.12 Microsoft Corporation
- 10.12.1 Global Overview
- 10.12.2 Market/Business Overview
- 10.12.3 Financial Data
- 10.12.3.1 Sales Revenue, 2022-2024
- 10.12.4 Product Landscape
- 10.12.5 Strategic Outlook
- 10.12.6 SWOT analysis
- 10.13 Palantir
- 10.13.1 Global Overview
- 10.13.2 Market/Business Overview
- 10.13.3 Financial Data
- 10.13.3.1 Sales Revenue, 2021-2024
- 10.13.4 Product Landscape
- 10.13.5 Strategic Outlook
- 10.13.6 SWOT Analysis
- 10.14 Petro.ai
- 10.14.1 Global Overview
- 10.14.2 Market/Business Overview
- 10.14.3 Financial Data
- 10.14.4 Product Landscape
- 10.14.5 SWOT Analysis
- 10.15 Rockwell Automation, Inc.
- 10.15.1 Global Overview
- 10.15.2 Market/Business Overview
- 10.15.3 Financial data
- 10.15.3.1 Sales Revenue, 2022-2024
- 10.15.4 Product Landscape
- 10.15.5 Strategic Outlook
- 10.15.7 SWOT analysis
- 1.16 Schlumberger
- 1.16.1 Global Overview
- 1.16.2 Market/Business Overview
- 1.16.3 Financial Data
- 1.16.3.1 Sales Revenue, 2022-2024
- 1.16.4 Product Landscape
- 1.16.5 Strategic Outlook
- 1.16.6 SWOT Analysis
- 1.17 Siemens Energy
- 1.17.1 Global Overview
- 1.17.2 Market/Business Overview
- 1.17.3 Financial Data
- 1.17.3.1 Sales Revenue, 2020-2023
- 1.17.4 Product Landscape
- 1.17.5 Strategic Outlook
- 1.17.6 SWOT analysis
- 1.18 SparkCognition
- 1.18.1 Global Overview
- 1.18.2 Market/Business Overview
- 1.18.3 Financial Data
- 1.18.4 Product Landscape
- 1.18.5 SWOT Analysis
- 1.19 Weatherford
- 1.19.1 Global Overview
- 1.19.2 Market/Business Overview
- 1.19.3 Financial Data
- 1.19.3.1 Sales Revenue, 2022-2024
- 1.19.4 Product Landscape
- 1.19.5 SWOT Analysis
- 1.20 Yokogawa
- 1.20.1 Global Overview
- 1.20.2 Market/Business Overview
- 1.20.3 Financial Data
- 1.20.3.1 Sales Revenue, 2021-2023
- 1.20.4 Product Landscape
- 1.20.5 Strategic Outlook
- 1.20.6 SWOT Analysis
- 1.21 Research practices
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