석유 및 가스 분야 AI 시장 : 유형별, 기능별, 용도별, 지역별(2025-2033년)

The global AI in oil and gas market size reached USD 2.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 6.4 Billion by 2033, exhibiting a growth rate (CAGR) of 8.2% during 2025-2033. The increasing data explosion in the oil and gas industry, rising demand for operational efficiency, growing emphasis on safety, imposition of strict environmental regulations, and the recent technological advancements in artificial intelligence (AI) algorithms are some of the major factors propelling the market.

AI in oil and gas refers to the application of artificial intelligence (AI) technologies in optimizing processes, enhancing safety, and improving decision-making in the oil and gas industry. It includes neural networks, computer visions, machine learning (ML), robotics, and natural language processing (NLP). AI in oil and gas is widely used in reservoir simulation, automated drilling, predictive maintenance, geological mapping, safety monitoring, process automation, and asset management. It aids in decision-making, reducing costs, increasing safety, improving reliability, enhancing scalability, and promoting sustainability.

The widespread adoption of AI to provide cost-effective solutions that can optimize resources and streamline operations across the oil and gas industry is propelling the market growth. Furthermore, the imposition of strict environmental regulations that are compelling firms to use AI for better compliance and to minimize their carbon footprint during oil and gas extraction is positively influencing the market growth. Additionally, the recent technological advancements in AI algorithms, coupled with increased computational power, which makes it more practical and efficient to implement AI-based solutions, are supporting the market growth. Besides this, the rising shortage of skilled personnel for complex tasks is facilitating the demand for AI to enable automation capabilities across various oil and gas drilling operations. Other factors, including the growing emphasis on sustainability, increasing demand for operational transparency, and rising oil and gas exploration, are anticipated to drive the market growth.

AI in Oil and Gas Market Trends/Drivers:

The increasing data explosion in the oil and gas industry

The oil and gas sector is generating an unprecedented volume of data stemming from sensors, drilling equipment, and various other operational technologies. As compared to traditional data analytics tools, AI is widely used to effectively manage and interpret this data in real-time, which is something. Furthermore, it offers advanced analytics capabilities that can browse through vast data sets to examine patterns, trends, and anomalies. Apart from this, AI provides the tools to transform raw data into useful insights that can be utilized for monitoring drilling operations and optimizing the supply chain processes. Moreover, companies are increasingly investing in AI solutions to make sense of their data and turn it into a strategic asset for smarter decision-making.

The rising demand for operational efficiency

The oil and gas sector involves complex, often hazardous operations that require meticulous planning and execution. Furthermore, human error, equipment failure, or inefficiencies in any part of the operation can result in significant financial losses or safety risks. In line with this, AI technologies, particularly machine learning (ML) and predictive analytics provide the capability to significantly optimize these operations. In addition, they can forecast equipment failures before they occur, automate repetitive tasks, and improve the precision of drilling and extraction processes. Moreover, AI not only reduces costs but also minimizes the risks associated with manual errors and system failures. As a result, operational efficiency is a major driving factor for the integration of AI in the oil and gas industry.

The growing emphasis on safety

The growing emphasis on safety in the oil and gas industry due to the hazardous nature of its operations, such as deep-sea drilling or working with highly flammable materials, is propelling the market growth. Furthermore, traditional safety measures often fall short of completely eliminating accidents and failures. In line with this, AI offers an advanced layer of safety protocols through real-time monitoring, predictive analytics, and automated control systems. It can analyze data from multiple sensors to detect irregularities that could signify a potential accident, enabling preventive actions to be taken before an incident occurs. Moreover, AI can automate certain high-risk tasks, reducing the need for manual intervention in potentially dangerous scenarios. As a result, the adoption of AI technologies for enhancing safety measures is a significant factor fueling the market growth.

AI in Oil and Gas Industry Segmentation:

Breakup by Type:

• Hardware
• Software
• Services

Software dominate the market

Software is dominating the market as it offers excellent flexibility and scalability, which make it highly adaptable to diverse operational needs. Furthermore, it can be easily updated to include new algorithms or features, ensuring that the oil and gas operations remain at the forefront of technological advancements. In addition, software solutions are more cost-effective in the long term, as they eliminate the need for heavy machinery or additional hardware installations. Besides this, it can be seamlessly integrated into existing systems, allowing for the centralization of data and processes. This harmonization significantly improves data analytics, enabling more accurate and timely decision-making. Moreover, software can be deployed across multiple sites, providing a unified approach to operations management. Apart from this, it can be continuously refined to address specific issues and opportunities presented by the oil and gas sector.

Breakup by Function:

• Predictive Maintenance and Machinery Inspection
• Material Movement
• Production Planning
• Field Services
• Quality Control
• Reclamation

Predictive maintenance and machinery inspection hold the largest share in the market

Predictive maintenance and machinery inspection are dominating the market as they aid in reducing downtime by analyzing equipment data and predicting failures before they happen. Furthermore, they help in identifying wear and tear or other forms of degradation that, if not addressed, could lead to serious safety issues. By preemptively identifying potential problems, companies can replace or repair components as needed, thereby improving the overall safety of operations. Additionally, the advancement in sensor technology and the Internet of Things (IoT), which has made data collection more robust and accurate, making predictive maintenance and machinery inspection increasingly reliable and effective, is positively influencing the market growth. Moreover, predictive maintenance and machinery inspection offers a strong return on investment (ROI), as they reduce maintenance costs, increase operational efficiency, and enhance security protocols.

Breakup by Application:

• Upstream
• Downstream
• Midstream

Upstream hold the largest share in the market

The upstream is dominating the market as it involves various complex and data-intensive tasks, such as drilling, exploration, and extraction of natural gas and crude oil. Furthermore, it requires extensive data analysis for geological interpretation and reservoir modeling to identify promising drilling locations. In addition, AI-based predictive analytics are widely used in upstream operations to forecast equipment failures, allowing for preemptive actions that can save both time and money. Besides this, AI-powered remote sensing technologies and robotics are widely utilized to perform critical tasks that are either hazardous for human workers or logistically challenging to manage, thereby enhancing safety and operational efficiency. Moreover, the widespread adoption of AI in the upstream sector due to the imposition of strict environmental regulations is favoring the market growth.

Breakup by Region:

• North America
• United States
• Canada
• Asia-Pacific
• China
• Japan
• India
• South Korea
• Australia
• Indonesia
• Others
• Europe
• Germany
• France
• United Kingdom
• Italy
• Spain
• Russia
• Others
• Latin America
• Brazil
• Mexico
• Others
• Middle East and Africa

North America exhibits a clear dominance, accounting for the largest AI in oil and gas market share

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America has a well-developed infrastructure for both oil and gas extraction and AI technology, making it easier for companies to adopt and integrate AI solutions. Furthermore, the escalating level of investment in research and innovation by regional governments and private players to ensure continuous development and implementation of AI in the oil and gas sector is positively influencing the market growth. Besides this, companies in North America have a more mature understanding of the value of data analytics. This data-driven culture is conducive to the acceptance and optimization of AI capabilities across various sectors, including oil and gas. Moreover, the easy availability of a skilled workforce trained in data sciences and AI algorithms, which facilitates the implementation of advanced technologies, is contributing to the market growth.

Competitive Landscape:

Leading companies are leveraging machine learning (ML), predictive analytics, and natural language processing (NLP) to optimize every aspect of the oil and gas lifecycle, from exploration and drilling to production and distribution. Additionally, they are forging strategic partnerships with technology providers, academic institutions, and competitors to accelerate innovation and share knowledge. Furthermore, they are focusing on gaining customer insights to address specific problems and offer tailored solutions, which aids in building trust and improving overall customer satisfaction. Besides this, market leaders are investing in pilot programs to test the practical applications of AI technologies before full-scale implementation. Moreover, the escalating emphasis on sustainability and environmental responsibility has prompted companies to build AI solutions that meet the stringent regulatory requirements of various regions and countries.

The report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

• Accenture plc
• C3.AI Inc.
• Cisco Systems Inc.
• Cloudera Inc.
• Fugenx Technologies
• Huawei Technologies Co. Ltd
• Infosys Limited
• Intel Corporation
• International Business Machines Corporation
• Microsoft Corporation
• Neudax
• Nvidia Corporation
• Oracle Corporation
• Shell plc.

Key Questions Answered in This Report

• 1.What was the size of the global AI in oil and gas market in 2024?
• 2.What is the expected growth rate of the global AI in oil and gas market during 2025-2033?
• 3.What are the key factors driving the global AI in oil and gas market?
• 4.What has been the impact of COVID-19 on the global AI in oil and gas market?
• 5.What is the breakup of the global AI in oil and gas market based on the type?
• 6.What is the breakup of the global AI in oil and gas market based on the function?
• 7.What is the breakup of the global AI in oil and gas market based on the application?
• 8.What are the key regions in the global AI in oil and gas market?
• 9.Who are the key players/companies in the global AI in oil and gas market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global AI in Oil and Gas Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Type

• 6.1 Hardware
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Software
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast
• 6.3 Services
  • 6.3.1 Market Trends
  • 6.3.2 Market Forecast

7 Market Breakup by Function

• 7.1 Predictive Maintenance and Machinery Inspection
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Material Movement
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Production Planning
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast
• 7.4 Field Services
  • 7.4.1 Market Trends
  • 7.4.2 Market Forecast
• 7.5 Quality Control
  • 7.5.1 Market Trends
  • 7.5.2 Market Forecast
• 7.6 Reclamation
  • 7.6.1 Market Trends
  • 7.6.2 Market Forecast

8 Market Breakup by Application

• 8.1 Upstream
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Downstream
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast
• 8.3 Midstream
  • 8.3.1 Market Trends
  • 8.3.2 Market Forecast

9 Market Breakup by Region

• 9.1 North America
  • 9.1.1 United States
    • 9.1.1.1 Market Trends
    • 9.1.1.2 Market Forecast
  • 9.1.2 Canada
    • 9.1.2.1 Market Trends
    • 9.1.2.2 Market Forecast
• 9.2 Asia-Pacific
  • 9.2.1 China
    • 9.2.1.1 Market Trends
    • 9.2.1.2 Market Forecast
  • 9.2.2 Japan
    • 9.2.2.1 Market Trends
    • 9.2.2.2 Market Forecast
  • 9.2.3 India
    • 9.2.3.1 Market Trends
    • 9.2.3.2 Market Forecast
  • 9.2.4 South Korea
    • 9.2.4.1 Market Trends
    • 9.2.4.2 Market Forecast
  • 9.2.5 Australia
    • 9.2.5.1 Market Trends
    • 9.2.5.2 Market Forecast
  • 9.2.6 Indonesia
    • 9.2.6.1 Market Trends
    • 9.2.6.2 Market Forecast
  • 9.2.7 Others
    • 9.2.7.1 Market Trends
    • 9.2.7.2 Market Forecast
• 9.3 Europe
  • 9.3.1 Germany
    • 9.3.1.1 Market Trends
    • 9.3.1.2 Market Forecast
  • 9.3.2 France
    • 9.3.2.1 Market Trends
    • 9.3.2.2 Market Forecast
  • 9.3.3 United Kingdom
    • 9.3.3.1 Market Trends
    • 9.3.3.2 Market Forecast
  • 9.3.4 Italy
    • 9.3.4.1 Market Trends
    • 9.3.4.2 Market Forecast
  • 9.3.5 Spain
    • 9.3.5.1 Market Trends
    • 9.3.5.2 Market Forecast
  • 9.3.6 Russia
    • 9.3.6.1 Market Trends
    • 9.3.6.2 Market Forecast
  • 9.3.7 Others
    • 9.3.7.1 Market Trends
    • 9.3.7.2 Market Forecast
• 9.4 Latin America
  • 9.4.1 Brazil
    • 9.4.1.1 Market Trends
    • 9.4.1.2 Market Forecast
  • 9.4.2 Mexico
    • 9.4.2.1 Market Trends
    • 9.4.2.2 Market Forecast
  • 9.4.3 Others
    • 9.4.3.1 Market Trends
    • 9.4.3.2 Market Forecast
• 9.5 Middle East and Africa
  • 9.5.1 Market Trends
  • 9.5.2 Market Breakup by Country
  • 9.5.3 Market Forecast

10 SWOT Analysis

• 10.1 Overview
• 10.2 Strengths
• 10.3 Weaknesses
• 10.4 Opportunities
• 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

• 12.1 Overview
• 12.2 Bargaining Power of Buyers
• 12.3 Bargaining Power of Suppliers
• 12.4 Degree of Competition
• 12.5 Threat of New Entrants
• 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

• 14.1 Market Structure
• 14.2 Key Players
• 14.3 Profiles of Key Players
  • 14.3.1 Accenture plc
    • 14.3.1.1 Company Overview
    • 14.3.1.2 Product Portfolio
    • 14.3.1.3 Financials
    • 14.3.1.4 SWOT Analysis
  • 14.3.2 C3.AI Inc.
    • 14.3.2.1 Company Overview
    • 14.3.2.2 Product Portfolio
    • 14.3.2.3 Financials
  • 14.3.3 Cisco Systems Inc.
    • 14.3.3.1 Company Overview
    • 14.3.3.2 Product Portfolio
    • 14.3.3.3 Financials
    • 14.3.3.4 SWOT Analysis
  • 14.3.4 Cloudera Inc.
    • 14.3.4.1 Company Overview
    • 14.3.4.2 Product Portfolio
  • 14.3.5 Fugenx Technologies
    • 14.3.5.1 Company Overview
    • 14.3.5.2 Product Portfolio
  • 14.3.6 Huawei Technologies Co. Ltd
    • 14.3.6.1 Company Overview
    • 14.3.6.2 Product Portfolio
    • 14.3.6.3 SWOT Analysis
  • 14.3.7 Infosys Limited
    • 14.3.7.1 Company Overview
    • 14.3.7.2 Product Portfolio
    • 14.3.7.3 Financials
    • 14.3.7.4 SWOT Analysis
  • 14.3.8 Intel Corporation
    • 14.3.8.1 Company Overview
    • 14.3.8.2 Product Portfolio
    • 14.3.8.3 Financials
    • 14.3.8.4 SWOT Analysis
  • 14.3.9 International Business Machines Corporation
    • 14.3.9.1 Company Overview
    • 14.3.9.2 Product Portfolio
    • 14.3.9.3 Financials
  • 14.3.10 Microsoft Corporation
    • 14.3.10.1 Company Overview
    • 14.3.10.2 Product Portfolio
    • 14.3.10.3 Financials
    • 14.3.10.4 SWOT Analysis
  • 14.3.11 Neudax
    • 14.3.11.1 Company Overview
    • 14.3.11.2 Product Portfolio
  • 14.3.12 Nvidia Corporation
    • 14.3.12.1 Company Overview
    • 14.3.12.2 Product Portfolio
    • 14.3.12.3 Financials
    • 14.3.12.4 SWOT Analysis
  • 14.3.13 Oracle Corporation
    • 14.3.13.1 Company Overview
    • 14.3.13.2 Product Portfolio
    • 14.3.13.3 Financials
    • 14.3.13.4 SWOT Analysis
  • 14.3.14 Shell plc
    • 14.3.14.1 Company Overview
    • 14.3.14.2 Product Portfolio
    • 14.3.14.3 Financials