AI in Oil and Gas Market Share, Trends & Forecast by 2034

AI in Oil and Gas Market Overview 2024 to 2034

The global AI in the oil and gas market is expected to register a CAGR of 14.1% during the forecast period. The market value is projected to increase from US$ 3.5 billion in 2024 to US$ 13 billion by 2034.

AI in Oil and Gas Market Size, Analysis, and Insights

The increasing demand for AI in the oil and gas industry is the need to improve operational efficiency. By using AI-powered systems to analyze data from sensors and other sources, oil and gas companies can identify inefficiencies in their operations and take steps to address them.
By analyzing data from sensors and other sources, AI can be used to identify potential safety hazards before they become a problem, allowing companies to take proactive measures to prevent accidents and injuries.
The need to reduce costs is expected to drive market growth in the forecast period. Leveraging AI to optimize operations and identify inefficiencies, companies can reduce their operating costs, improving their profitability in an increasingly competitive market.
The oil and gas industry has always been one of the data-intensive industries, generating massive amounts of data daily. With the advent of digitalization and the Internet of Things (IoT), this data generation has only increased further, leading to the need for more advanced technologies to help manage and analyze this data.

Attributes	Details
Market Size, 2024	US$ 3.5 billion
Market Value, 2034	US$ 13 billion
CAGR (2024 to 2034)	14.1%

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Key Opportunities for the AI in Oil and Gas Market

AI is being used in the oil and gas industry in exploration, and production is expected to create significant opportunities in the market. For example, it can analyze seismic data to identify potential oil and gas reserves with greater accuracy and speed than traditional methods. This allows companies to make better decisions about where to drill and how to extract resources from the ground.
By analyzing data from sensors and other sources, AI can help companies predict when equipment is likely to fail and schedule maintenance accordingly. This reduces downtime and maintenance costs and can also improve safety by reducing the risk of equipment failure.
AI can help companies improve safety by identifying potential hazards and risks before they occur. The use of AI in the oil and gas industry offers numerous opportunities for companies to improve their operations, reduce costs, and improve safety. As the technology continues to advance, we can expect to see even more innovative applications of AI in the industry.

Challenges for the AI in Oil and Gas Market

The lack of data quality and availability is an ongoing challenge in the market. For AI algorithms to work effectively, they require large amounts of high-quality data. The oil and gas industry, however, has historically struggled with data silos, incomplete datasets, and a lack of standardization.
Oil and gas exploration and production involve a wide range of processes, from drilling and reservoir modeling to refining and distribution. Each of these processes generates a massive amount of data, making it difficult for AI models to work across the entire value chain.
The deployment of AI in the oil and gas industry requires significant investment in infrastructure, hardware, and software. This can be a barrier for many companies, particularly the new entrants, who can be short of such resources and investments.

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Category-wise Insights

The Solution Segment Dominates the Market

Attributes	Details
Component	Solution
Market CAGR From 2024 to 2034	13.8%

There is an increasing demand for AI in data science solutions that can optimize operations and reduce costs. AI-powered solutions can help predict equipment failures, optimize production, and minimize downtime.
They can also improve safety by identifying potential hazards and taking corrective actions. AI solutions can help in automating routine tasks and assist in decision-making processes. These benefits have led to an increase in the adoption of AI-powered solutions in the oil and gas industry.

The Upstream Segment Dominates the Market by Operation

Attributes	Details
By Operation	Upstream
Market CAGR from 2024 to 2034	13.6%

The upstream is critical to the industry as it identifies new oil and gas reserves, increases production, and maintains existing fields.
Companies in this segment are constantly investing in new technologies, including AI, to improve exploration and production efficiency, reduce greenhouse emissions, and enhance asset economic life. Moreover, companies are applying advanced analytics and gas-lift efficiency to improve productivity and equipment maintenance.

Country-wise insights

Countries	CAGR from 2024 to 2034
The United States	14.5%
The United Kingdom	15.7%
China	13.4%
Japan	15.4%
South Korea	12.3%

Increasing Need for Cost-Cutting and Improve Efficiency in the United States

The oil and gas industry in the United States is facing increased pressure to improve efficiency and reduce costs while also ensuring worker safety and environmental responsibility. Artificial intelligence (AI) has emerged as a key solution to these challenges, leading to an increasing demand for AI in the industry.
The shale boom has led to an increase in production, which has put pressure on companies to improve their operations in the United States. AI can help companies optimize production and reduce costs, enabling them to remain competitive in the market. Moreover, AI can help companies preserve and transfer this knowledge to the next generation of workers.

Rising Awareness Regarding Carbon Emission in the United Kingdom Driving Growth

With the increasing energy demand in the United Kingdom, the industry is constantly looking for ways to improve efficiency and reduce costs. AI-powered systems can analyze large volumes of data, identify patterns, and make predictions that help optimize production, reduce downtime, and improve safety.
The United Kingdom has been investing heavily in AI research and development, making it a hotbed for innovation in the field. The country has a well-established network of universities, research centers, and startups pushing AI's boundaries. This has led to the creation of cutting-edge AI technologies that are now being applied to the oil and gas industry.
Increasing awareness of the need to reduce carbon emissions. AI-powered systems can help optimize operations and reduce waste, leading to a more sustainable and environmentally friendly industry.

The Rapid Growth of the Tech Industry in Advancin the Market of AI in Oil and Gas in China

The country is one of the leading consumers of oil and gas in the world, which means that there is significant interest in finding ways to improve the efficiency and safety of these operations. AI can help achieve these goals by optimizing production processes, predicting equipment failures, and improving safety protocols.
The Chinese government has been actively promoting the development and adoption of AI technologies, recognizing their potential to transform various industries, including oil and gas. This has resulted in increased investment and research in the field, as well as the creation of favorable policies and regulations.
The rapid growth of China's tech industry has led to the emergence of several AI startups that are focused on developing solutions for the oil and gas sector. These companies are leveraging China's vast data resources and expertise in machine learning to create innovative tools and applications that can improve the performance and profitability of oil and gas operations.

Need to Optimize Energy Production and Management in Japan

The oil and gas industry in Japan is a significant contributor to the country's economy, accounting for a large share of its domestic energy supply. In recent years, there has been a growing demand for AI technology in the oil and gas sector as Japan seeks to improve its energy efficiency and reduce carbon emissions.
The need to optimize energy production and management is raising the demand for AI-powered solutions that can help identify patterns and trends in data from oil and gas fields, enabling companies to make more informed decisions about where to drill, how to extract resources, and when to invest in new infrastructure.
AI can also be used to monitor equipment performance, detect potential failures, and improve maintenance planning, which can help reduce downtime and increase productivity. Moreover, AI-powered algorithms can help identify patterns and trends in data from oil and gas fields, enabling companies to make more informed decisions about where to drill, how to extract resources, and when to invest in new infrastructure.

Rising Government Initiatives Toward Technological Advancement in South Korea

South Korea is known for its technological advancements and innovation. The government has been promoting the development and adoption of AI technology in various industries, including oil and gas.
Many companies in South Korea are already using AI-powered solutions for upstream, midstream, and downstream operations. The growing need for better safety measures is driving the demand for AI in the sector, as it can help prevent accidents and improve the overall safety of workers and the environment.
South Korea has been a significant player in the oil and gas market for several decades. Therefore, the country is investing in AI to improve its competitiveness in the industry.

Competitive Landscape

Many companies are investing in AI technologies to optimize their operations, reduce costs, and improve safety. Companies are investing heavily in AI-powered solutions that can analyze data from sensors and other sources to improve efficiency and reduce downtime.

Additionally, there are many startups and smaller companies that are developing innovative AI solutions for the oil and gas industry. Overall, the market is moderately competitive, with many players vying for a significant share.

Recent Developments

In 2022, Oil and Gas Holding Co. (no holding) and AIQ teamed up to bring digital solutions and artificial intelligence into their upstream operations. This collaboration helps nogaholding improve its operational efficiency by utilizing advanced AI technologies provided by AIQ.
In 2023, C3 AI launched the C3 Generative AI Product Suite, which includes the C3 Generative AI for Enterprise Search as its first product. The suite also offers advanced transformer models, making it easier for businesses to use AI throughout their value chains. This suite has pre-built AI applications for various industries, including the oil and gas sector, to speed up transformation efforts across different business functions.

Key Market Players

FuGenX Technologies Pvt. Ltd
Microsoft Corporation
IBM Corp.
Google LLC
Intel Corporation

Key Market Segments

By Component:

Solution
Services

By Operation:

Upstream
Midstream
Downstream

By Region:

North America
Latin America
East Asia
South Asia
Europe
Oceania
The Middle East and Africa

Frequently Asked Questions

What is the Outlook for the Market in China?

The market in China is expected to progress at a CAGR of 13.4% through the forecast period.

How Big is the AI in Oil and Gas Market in 2024?

The market for AI in oil and gas is valued at around US$ 3.5 billion in 20224.

Which Component Type Segment Contributes More to the AI in Oil and Gas Market?

The solution segment progressed 13.6% of the market CAGR from 2024 to 2034.

What is the United States Market Outlook?

The United States accounts for around 14.5% of the CAGR from 2024 to 2034.

What is the Global Market Projected Value for 2034?

The market is set to reach a valuation of around US$ 13 billion by the end of 2034.

What is the Growth Potential of the Market?

The market is estimated to register a growth of 14.1% from 2024 to 2034.

Table of Content

1. Executive Summary

1.1. Global Market Outlook

1.2. Demand-side Trends

1.3. Supply-side Trends

1.4. Technology Roadmap Analysis

1.5. Analysis and Recommendations

2. Market Overview

2.1. Market Coverage / Taxonomy

2.2. Market Definition / Scope / Limitations

3. Market Background

3.1. Market Dynamics

3.1.1. Drivers

3.1.2. Restraints

3.1.3. Opportunity

3.1.4. Trends

3.2. Scenario Forecast

3.2.1. Demand in Optimistic Scenario

3.2.2. Demand in Likely Scenario

3.2.3. Demand in Conservative Scenario

3.3. Opportunity Map Analysis

3.4. Investment Feasibility Matrix

3.5. PESTLE and Porter’s Analysis

3.6. Regulatory Landscape

3.6.1. By Key Regions

3.6.2. By Key Countries

3.7. Regional Parent Market Outlook

4. Global Market Analysis 2019 to 2023 and Forecast, 2024 to 2034

4.1. Historical Market Size Value (US$ Million) Analysis, 2019 to 2023

4.2. Current and Future Market Size Value (US$ Million) Projections, 2024 to 2034

4.2.1. Y-o-Y Growth Trend Analysis

4.2.2. Absolute $ Opportunity Analysis

5. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Component

5.1. Introduction / Key Findings

5.2. Historical Market Size Value (US$ Million) Analysis By Component, 2019 to 2023

5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Component, 2024 to 2034

5.3.1. Solution

5.3.2. Services

5.4. Y-o-Y Growth Trend Analysis By Component, 2019 to 2023

5.5. Absolute $ Opportunity Analysis By Component, 2024 to 2034

6. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Operation

6.1. Introduction / Key Findings

6.2. Historical Market Size Value (US$ Million) Analysis By Operation, 2019 to 2023

6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Operation, 2024 to 2034

6.3.1. Upstream

6.3.2. Midstream

6.3.3. Downstream

6.4. Y-o-Y Growth Trend Analysis By Operation, 2019 to 2023

6.5. Absolute $ Opportunity Analysis By Operation, 2024 to 2034

7. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Region

7.1. Introduction

7.2. Historical Market Size Value (US$ Million) Analysis By Region, 2019 to 2023

7.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2024 to 2034

7.3.1. North America

7.3.2. Latin America

7.3.3. Western Europe

7.3.4. Eastern Europe

7.3.5. South Asia and Pacific

7.3.6. East Asia

7.3.7. Middle East and Africa

7.4. Market Attractiveness Analysis By Region

8. North America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

8.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

8.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

8.2.1. By Country

8.2.1.1. USA

8.2.1.2. Canada

8.2.2. By Component

8.2.3. By Operation

8.3. Market Attractiveness Analysis

8.3.1. By Country

8.3.2. By Component

8.3.3. By Operation

8.4. Key Takeaways

9. Latin America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

9.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

9.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

9.2.1. By Country

9.2.1.1. Brazil

9.2.1.2. Mexico

9.2.1.3. Rest of Latin America

9.2.2. By Component

9.2.3. By Operation

9.3. Market Attractiveness Analysis

9.3.1. By Country

9.3.2. By Component

9.3.3. By Operation

9.4. Key Takeaways

10. Western Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

10.2.1. By Country

10.2.1.1. Germany

10.2.1.2. UK

10.2.1.3. France

10.2.1.4. Spain

10.2.1.5. Italy

10.2.1.6. Rest of Western Europe

10.2.2. By Component

10.2.3. By Operation

10.3. Market Attractiveness Analysis

10.3.1. By Country

10.3.2. By Component

10.3.3. By Operation

10.4. Key Takeaways

11. Eastern Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

11.2.1. By Country

11.2.1.1. Poland

11.2.1.2. Russia

11.2.1.3. Czech Republic

11.2.1.4. Romania

11.2.1.5. Rest of Eastern Europe

11.2.2. By Component

11.2.3. By Operation

11.3. Market Attractiveness Analysis

11.3.1. By Country

11.3.2. By Component

11.3.3. By Operation

11.4. Key Takeaways

12. South Asia and Pacific Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

12.2.1. By Country

12.2.1.1. India

12.2.1.2. Bangladesh

12.2.1.3. Australia

12.2.1.4. New Zealand

12.2.1.5. Rest of South Asia and Pacific

12.2.2. By Component

12.2.3. By Operation

12.3. Market Attractiveness Analysis

12.3.1. By Country

12.3.2. By Component

12.3.3. By Operation

12.4. Key Takeaways

13. East Asia Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

13.2.1. By Country

13.2.1.1. China

13.2.1.2. Japan

13.2.1.3. South Korea

13.2.2. By Component

13.2.3. By Operation

13.3. Market Attractiveness Analysis

13.3.1. By Country

13.3.2. By Component

13.3.3. By Operation

13.4. Key Takeaways

14. Middle East and Africa Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country

14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023

14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034

14.2.1. By Country

14.2.1.1. GCC Countries

14.2.1.2. South Africa

14.2.1.3. Israel

14.2.1.4. Rest of MEA

14.2.2. By Component

14.2.3. By Operation

14.3. Market Attractiveness Analysis

14.3.1. By Country

14.3.2. By Component

14.3.3. By Operation

14.4. Key Takeaways

15. Key Countries Market Analysis

15.1. USA

15.1.1. Pricing Analysis

15.1.2. Market Share Analysis, 2023

15.1.2.1. By Component

15.1.2.2. By Operation

15.2. Canada

15.2.1. Pricing Analysis

15.2.2. Market Share Analysis, 2023

15.2.2.1. By Component

15.2.2.2. By Operation

15.3. Brazil

15.3.1. Pricing Analysis

15.3.2. Market Share Analysis, 2023

15.3.2.1. By Component

15.3.2.2. By Operation

15.4. Mexico

15.4.1. Pricing Analysis

15.4.2. Market Share Analysis, 2023

15.4.2.1. By Component

15.4.2.2. By Operation

15.5. Germany

15.5.1. Pricing Analysis

15.5.2. Market Share Analysis, 2023

15.5.2.1. By Component

15.5.2.2. By Operation

15.6. UK

15.6.1. Pricing Analysis

15.6.2. Market Share Analysis, 2023

15.6.2.1. By Component

15.6.2.2. By Operation

15.7. France

15.7.1. Pricing Analysis

15.7.2. Market Share Analysis, 2023

15.7.2.1. By Component

15.7.2.2. By Operation

15.8. Spain

15.8.1. Pricing Analysis

15.8.2. Market Share Analysis, 2023

15.8.2.1. By Component

15.8.2.2. By Operation

15.9. Italy

15.9.1. Pricing Analysis

15.9.2. Market Share Analysis, 2023

15.9.2.1. By Component

15.9.2.2. By Operation

15.10. Poland

15.10.1. Pricing Analysis

15.10.2. Market Share Analysis, 2023

15.10.2.1. By Component

15.10.2.2. By Operation

15.11. Russia

15.11.1. Pricing Analysis

15.11.2. Market Share Analysis, 2023

15.11.2.1. By Component

15.11.2.2. By Operation

15.12. Czech Republic

15.12.1. Pricing Analysis

15.12.2. Market Share Analysis, 2023

15.12.2.1. By Component

15.12.2.2. By Operation

15.13. Romania

15.13.1. Pricing Analysis

15.13.2. Market Share Analysis, 2023

15.13.2.1. By Component

15.13.2.2. By Operation

15.14. India

15.14.1. Pricing Analysis

15.14.2. Market Share Analysis, 2023

15.14.2.1. By Component

15.14.2.2. By Operation

15.15. Bangladesh

15.15.1. Pricing Analysis

15.15.2. Market Share Analysis, 2023

15.15.2.1. By Component

15.15.2.2. By Operation

15.16. Australia

15.16.1. Pricing Analysis

15.16.2. Market Share Analysis, 2023

15.16.2.1. By Component

15.16.2.2. By Operation

15.17. New Zealand

15.17.1. Pricing Analysis

15.17.2. Market Share Analysis, 2023

15.17.2.1. By Component

15.17.2.2. By Operation

15.18. China

15.18.1. Pricing Analysis

15.18.2. Market Share Analysis, 2023

15.18.2.1. By Component

15.18.2.2. By Operation

15.19. Japan

15.19.1. Pricing Analysis

15.19.2. Market Share Analysis, 2023

15.19.2.1. By Component

15.19.2.2. By Operation

15.20. South Korea

15.20.1. Pricing Analysis

15.20.2. Market Share Analysis, 2023

15.20.2.1. By Component

15.20.2.2. By Operation

15.21. GCC Countries

15.21.1. Pricing Analysis

15.21.2. Market Share Analysis, 2023

15.21.2.1. By Component

15.21.2.2. By Operation

15.22. South Africa

15.22.1. Pricing Analysis

15.22.2. Market Share Analysis, 2023

15.22.2.1. By Component

15.22.2.2. By Operation

15.23. Israel

15.23.1. Pricing Analysis

15.23.2. Market Share Analysis, 2023

15.23.2.1. By Component

15.23.2.2. By Operation

16. Market Structure Analysis

16.1. Competition Dashboard

16.2. Competition Benchmarking

16.3. Market Share Analysis of Top Players

16.3.1. By Regional

16.3.2. By Component

16.3.3. By Operation

17. Competition Analysis

17.1. Competition Deep Dive

17.1.1. Cloudera, Inc

17.1.1.1. Overview

17.1.1.2. Product Portfolio

17.1.1.3. Profitability by Market Segments

17.1.1.4. Sales Footprint

17.1.1.5. Strategy Overview

17.1.1.5.1. Marketing Strategy

17.1.2. Oracle

17.1.2.1. Overview

17.1.2.2. Product Portfolio

17.1.2.3. Profitability by Market Segments

17.1.2.4. Sales Footprint

17.1.2.5. Strategy Overview

17.1.2.5.1. Marketing Strategy

17.1.3. Google LLC

17.1.3.1. Overview

17.1.3.2. Product Portfolio

17.1.3.3. Profitability by Market Segments

17.1.3.4. Sales Footprint

17.1.3.5. Strategy Overview

17.1.3.5.1. Marketing Strategy

17.1.4. Microsoft Corporation

17.1.4.1. Overview

17.1.4.2. Product Portfolio

17.1.4.3. Profitability by Market Segments

17.1.4.4. Sales Footprint

17.1.4.5. Strategy Overview

17.1.4.5.1. Marketing Strategy

17.1.5. FuGenX Technologies Pvt. Ltd , Inc

17.1.5.1. Overview

17.1.5.2. Product Portfolio

17.1.5.3. Profitability by Market Segments

17.1.5.4. Sales Footprint

17.1.5.5. Strategy Overview

17.1.5.5.1. Marketing Strategy

17.1.6. Intel Corporation

17.1.6.1. Overview

17.1.6.2. Product Portfolio

17.1.6.3. Profitability by Market Segments

17.1.6.4. Sales Footprint

17.1.6.5. Strategy Overview

17.1.6.5.1. Marketing Strategy

17.1.7. Cisco Systems, Inc

17.1.7.1. Overview

17.1.7.2. Product Portfolio

17.1.7.3. Profitability by Market Segments

17.1.7.4. Sales Footprint

17.1.7.5. Strategy Overview

17.1.7.5.1. Marketing Strategy

17.1.8. IBM

17.1.8.1. Overview

17.1.8.2. Product Portfolio

17.1.8.3. Profitability by Market Segments

17.1.8.4. Sales Footprint

17.1.8.5. Strategy Overview

17.1.8.5.1. Marketing Strategy

17.1.9. NVIDIA Corporation

17.1.9.1. Overview

17.1.9.2. Product Portfolio

17.1.9.3. Profitability by Market Segments

17.1.9.4. Sales Footprint

17.1.9.5. Strategy Overview

17.1.9.5.1. Marketing Strategy

17.1.10. C3.ai, Inc

17.1.10.1. Overview

17.1.10.2. Product Portfolio

17.1.10.3. Profitability by Market Segments

17.1.10.4. Sales Footprint

17.1.10.5. Strategy Overview

17.1.10.5.1. Marketing Strategy

18. Assumptions & Acronyms Used

19. Research Methodology

AI in Oil and Gas Market

AI in Oil and Gas Market Forecast by Upstream and Downstream Operation for 2024 to 2034