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市场调查报告书

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1639305

石油和天然气市场机会、成长动力、产业趋势分析和预测 2025 - 2034 年人工智慧和机器学习

AI and ML in Oil and Gas Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034

出版日期: 2024年11月29日 | 出版商:

Global Market Insights Inc. | 英文 160 Pages | 商品交期: 2-3个工作天内

价格

简介目录

2024 年，全球石油和天然气领域的人工智慧和机器学习市场估值为 25 亿美元，预计 2025 年至 2034 年复合年增长率为 7.1%。营运效率提高，预测分析的重要性日益提高。公司正在利用人工智慧和机器学习技术来优化流程、降低成本并改善行业内各种营运的决策。

人工智慧和机器学习的采用正在重塑石油和天然气产业，在效率、成本管理和安全合规性方面带来显着优势。这些技术在确保更好的营运绩效、更高的安全标准和更强的环境合规性方面发挥关键作用，使其成为现代石油和天然气营运不可或缺的一部分。

对高阶分析的需求不断增长，进一步推动了市场的发展，这对于从地理空间、地震和营运资讯等大型资料集中提取可操作的见解至关重要。透过利用这些见解，公司可以加强勘探、钻井、生产和维护活动，从而改善决策并最大限度地减少停机时间。石油和天然气分析市场也在不断增长，预计到 2030 年收入将达到约 330 亿美元，年增长率超过 20%。

市场范围
开始年份	2024年
预测年份	2025-2034
起始值	25亿美元
预测值	49 亿美元
复合年增长率	7.1%

在营运方面，市场分为上游、中游和下游活动。由于人工智慧驱动的地震解释、井规划和生产优化解决方案的集成，上游领域的价值到 2024 年将超过 10 亿美元，正在经历快速增长。这些进步实现了即时决策，从而提高了勘探和生产过程的效率并减少了停机时间。

人工智慧和机器学习在石油和天然气行业的应用范围涵盖勘探和生产（E＆P）优化、油藏管理、钻井优化、资产监控、管道监控和供应链优化等领域。在人工智慧技术的推动下，勘探与生产优化领域预计在预测期内将以超过 5% 的复合年增长率成长，这些技术可改善资源识别、提高效能并降低营运风险。

从地区来看，北美在 2024 年占据市场主导地位，占营收份额的 30%。该地区发达的能源产业和对数位解决方案的大量投资加速了人工智慧技术的采用，特别是在预测性维护、钻井优化和油藏管理方面，所有这些都优先考虑成本效率和永续性。

产业生态系统分析
供应商格局
- 技术提供者
- 平台提供者
- 石油和天然气营运商
- 经销商
- 最终用户
利润率分析
技术与创新格局
专利分析
监管环境
二手案例
- 使用案例1
  - 好处
  - 投资报酬率
- 使用案例2
  - 好处
  - 投资报酬率
案例研究
- 案例研究1
  - 消费者姓名
  - 挑战
  - 解决方案
  - 影响
- 案例研究2
  - 消费者姓名
  - 挑战
  - 解决方案
  - 影响
衝击力
- 成长动力
  - 对营运效率的需求不断增长
  - 越来越多地采用预测性维护
  - 越来越重视数据驱动的决策
  - 数位转型投资不断增加
- 产业陷阱与挑战
  - 数据品质和整合挑战
  - 熟练劳动力短缺
成长潜力分析
波特的分析
PESTEL分析

第 4 章：竞争格局

介绍
公司市占率分析
竞争定位矩阵
战略展望矩阵

第 5 章：市场估计与预测：依产品分类，2021 - 2034 年

主要趋势
平台
服务

第 6 章：市场估计与预测：依营运状况，2021 - 2034 年

主要趋势
上游
中游
下游

第 7 章：市场估计与预测：按应用分类，2021 - 2034 年

主要趋势
勘探与生产 (E&P) 最佳化
水库管理
钻井优化
资产监控与管理
管道监测和洩漏检测
供应链优化
其他的

第 8 章：市场估计与预测：依最终用途，2021 - 2034 年

主要趋势
国家石油公司 (NOC)
独立石油公司 (IOC)

第 9 章：市场估计与预测：按地区，2021 - 2034 年

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 英国
- 德国
- 法国
- 义大利
- 西班牙
- 俄罗斯
- 北欧人
亚太地区
- 中国
- 印度
- 日本
- 澳洲
- 韩国
- 东南亚
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
MEA
- 阿联酋
- 南非
- 沙乌地阿拉伯

第 10 章：公司简介

ABB
Ambyint
Aspen Technology
Baker Hughes
C3.ai
Dataiku
Emerson Electric
Halliburton
Honeywell
IBM
Intel
Microsoft
Palantir
Petro.ai
Rockwell Automation
Schlumberger
Siemens Energy
SparkCognition
Weatherford
Yokogawa Electric

简介目录

Product Code: 12453

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%.

Market Scope
Start Year	2024
Forecast Year	2025-2034
Start Value	$2.5 Billion
Forecast Value	$4.9 Billion
CAGR	7.1%

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.

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