查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1527280

全球石油和天然气人工智慧市场规模研究，按组成部分（解决方案、服务）、营运（上游、中游、下游）和 2022-2032 年区域预测

Global AI in Oil and Gas Market Size Study, by Component (Solution, Services), by Operation (Upstream, Midstream, Downstream), and Regional Forecasts 2022-2032
出版日期: | 出版商: Bizwit Research & Consulting LLP | 英文 285 Pages | 商品交期: 2-3个工作天内

价格
简介目录

预计到 2023 年，全球人工智慧在石油和天然气市场的价值约为 35 亿美元，预计 2024 年至 2032 年将以 14.1% 的年复合成长率(CAGR) 成长，最终达到 130 亿美元的估值到2032年底。它可以比传统方法更准确、更快速地分析地震资料来识别潜在的石油和天然气储量。这种能力使公司能够就钻井和资源开采做出更好的决策。此外，人工智慧透过分析感测器资料来预测设备故障，从而协助预测性维护，从而减少停机时间、维护成本并提高安全性。

石油和天然气产业越来越多地采用人工智慧（AI），主要是出于提高营运效率的需要。人工智慧驱动的系统分析来自感测器和其他来源的资料，以识别效率低下的情况，使公司能够采取纠正措施。此外，人工智慧在识别潜在安全隐患方面发挥着至关重要的作用，可以采取主动措施来防止事故和伤害。透过优化营运和识别效率低下的情况，人工智慧可以帮助企业在竞争激烈的市场中降低营运成本并提高获利能力。此外，石油和天然气产业越来越多地采用人工智慧（AI）主要是出于提高营运效率的需要。人工智慧驱动的系统分析来自感测器和其他来源的资料，以识别效率低下的情况，使公司能够采取纠正措施。此外，人工智慧在识别潜在安全隐患方面发挥着至关重要的作用，可以采取主动措施来防止事故和伤害。透过优化营运和识别低效率，人工智慧可以帮助企业在竞争激烈的市场中降低营运成本并提高获利能力。儘管有这些好处，但资料品质和可用性等挑战仍然存在。高品质的资料对于人工智慧演算法的有效运作至关重要。石油和天然气产业历来面临资料孤岛、资料集不完整和缺乏标准化的问题，使得人工智慧模型很难在整个价值链上发挥作用。

全球石油和天然气人工智慧市场研究考虑的关键区域包括亚太地区、北美、欧洲、拉丁美洲和世界其他地区。北美是石油和天然气领域人工智慧的领先市场，其推动因素包括其强劲的经济、人工智慧技术的广泛采用、顶级人工智慧软体和系统供应商的大量存在，以及政府和私人实体在研发方面的联合投资。该地区石油和天然气生产能力的扩大和投资的增加预计将进一步增加市场机会。

目录

第 1 章：石油和天然气市场中的全球人工智慧执行摘要

  • 全球人工智慧在石油和天然气市场的规模及预测（2022-2032）
  • 区域概要
  • 分部摘要
    • 按组件
    • 按操作
  • 主要趋势
  • 经济衰退的影响
  • 分析师推荐与结论

第 2 章：全球人工智慧在石油和天然气市场的定义和研究假设

  • 研究目的
  • 市场定义
  • 研究假设
    • 包容与排除
    • 限制
    • 供给侧分析
      • 可用性
      • 基础设施
      • 监管环境
      • 市场竞争
      • 经济可行性（消费者的角度）
    • 需求面分析
      • 监理框架
      • 技术进步
      • 环境考虑
      • 消费者意识和接受度
  • 估算方法
  • 研究考虑的年份
  • 货币兑换率

第 3 章：全球人工智慧在石油和天然气市场动态中的应用

  • 市场驱动因素
    • 对营运效率的需求不断增加
    • 安全增强和危险预防
    • 降低成本倡议
  • 市场挑战
    • 数据品质和可用性问题
    • 整个价值链的复杂性
  • 市场机会
    • 勘探和生产中的人工智慧
    • 预测性维护和减少停机时间
    • 安全领域的先进人工智慧应用

第 4 章：全球人工智慧在石油和天然气市场的产业分析

  • 波特的五力模型
    • 供应商的议价能力
    • 买家的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争竞争
    • 波特五力模型的未来方法
    • 波特的 5 力影响分析
  • PESTEL分析
    • 政治的
    • 经济
    • 社会的
    • 技术性
    • 环境的
    • 合法的
  • 顶级投资机会
  • 最佳制胜策略
  • 颠覆性趋势
  • 产业专家视角
  • 分析师推荐与结论

第 5 章：全球人工智慧在石油和天然气市场的规模和预测：按组成部分 - 2022-2032

  • 细分仪表板
  • 石油和天然气市场中的全球人工智慧：2022 年和 2032 年组件收入趋势分析
    • 解决方案
    • 服务

第 6 章：全球人工智慧在石油和天然气市场的规模和预测：按营运划分 - 2022-2032

  • 细分仪表板
  • 全球石油和天然气市场人工智慧：2022年和2032年营运收入趋势分析
    • 上游
    • 中游
    • 下游

第 7 章：全球人工智慧在石油和天然气市场的规模和预测：按地区 - 2022-2032

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 欧洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 亚太地区其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 南非
    • 中东和非洲其他地区

第 8 章：竞争情报

  • 重点企业SWOT分析
  • 顶级市场策略
  • 公司简介
    • Baker Hughes
      • 关键讯息
      • 概述
      • 财务（视数据可用性而定）
      • 产品概要
      • 市场策略
    • Microsoft
    • C3.ai
    • Siemens
    • Honeywell
    • Oracle
    • Accenture
    • Google Cloud
    • Rockwell Automation
    • Infosys
    • TIBCO Software
    • ABB
    • IBM
    • Schlumberger
    • Halliburton

第 9 章：研究过程

  • 研究过程
    • 资料探勘
    • 分析
    • 市场预测
    • 验证
    • 出版
  • 研究属性
简介目录

Global AI in Oil and Gas Market is estimated to be valued at approximately USD 3.5 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 14.1% from 2024 to 2032, ultimately reaching a valuation of USD 13 billion by the end of 2032. AI is also being utilized in exploration and production within the oil and gas sector. It can analyze seismic data to identify potential oil and gas reserves more accurately and quickly than traditional methods. This capability allows companies to make better decisions regarding drilling and resource extraction. Furthermore, AI assists in predictive maintenance by analyzing sensor data to predict equipment failures, thereby reducing downtime, maintenance costs, and improving safety.

The increasing adoption of artificial intelligence (AI) in the oil and gas industry is primarily driven by the need to enhance operational efficiency. AI-powered systems analyze data from sensors and other sources to identify inefficiencies, enabling companies to take corrective actions. Additionally, AI plays a crucial role in identifying potential safety hazards, allowing for proactive measures to prevent accidents and injuries. By optimizing operations and identifying inefficiencies, AI helps companies reduce operating costs and improve profitability in a highly competitive market. Also, the increasing adoption of artificial intelligence (AI) in the oil and gas industry is primarily driven by the need to enhance operational efficiency. AI-powered systems analyze data from sensors and other sources to identify inefficiencies, enabling companies to take corrective actions. Additionally, AI plays a crucial role in identifying potential safety hazards, allowing for proactive measures to prevent accidents and injuries. By optimizing operations and identifying inefficiencies, AI helps companies reduce operating costs and improve profitability in a highly competitive market. Despite these benefits, challenges such as data quality and availability persist. High-quality data is essential for AI algorithms to function effectively. The oil and gas industry has historically faced issues with data silos, incomplete datasets, and a lack of standardization, making it difficult for AI models to work across the entire value chain.

Key regions considered for the Global AI in Oil and Gas market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America is a leading market for AI in the oil and gas sector, driven by its strong economy, widespread adoption of AI technologies, significant presence of top AI software and system suppliers, and joint investments by government and private entities in research and development. The region's expanding oil and gas production capacities and rising investments are expected to further enhance market opportunities.

Major market players included in this report are:

  • IBM
  • Schlumberger
  • Halliburton
  • Baker Hughes
  • Microsoft
  • C3.ai
  • Siemens
  • Honeywell
  • Oracle
  • Accenture
  • Google Cloud
  • Rockwell Automation
  • Infosys
  • TIBCO Software
  • ABB

The detailed segments and sub-segment of the market are explained below:

By Component:

  • Solution
  • Services

By Operation:

  • Upstream
  • Midstream
  • Downstream

By Region:

  • North America
  • U.S.
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Spain
  • Italy
  • ROE
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • RoAPAC
  • Latin America
  • Brazil
  • Mexico
  • Middle East & Africa
  • Saudi Arabia
  • South Africa
  • RoMEA

Years considered for the study are as follows:

  • Historical year - 2022
  • Base year - 2023
  • Forecast period - 2024 to 2032

Key Takeaways:

  • Market Estimates & Forecast for 10 years from 2022 to 2032.
  • Annualized revenues and regional level analysis for each market segment.
  • Detailed analysis of geographical landscape with Country level analysis of major regions.
  • Competitive landscape with information on major players in the market.
  • Analysis of key business strategies and recommendations on future market approach.
  • Analysis of competitive structure of the market.
  • Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global AI in the Oil and Gas Market Executive Summary

  • 1.1. Global AI in the Oil and Gas Market Size & Forecast (2022-2032)
  • 1.2. Regional Summary
  • 1.3. Segmental Summary
    • 1.3.1. By Component
    • 1.3.2. By Operation
  • 1.4. Key Trends
  • 1.5. Recession Impact
  • 1.6. Analyst Recommendation & Conclusion

Chapter 2. Global AI in the Oil and Gas Market Definition and Research Assumptions

  • 2.1. Research Objective
  • 2.2. Market Definition
  • 2.3. Research Assumptions
    • 2.3.1. Inclusion & Exclusion
    • 2.3.2. Limitations
    • 2.3.3. Supply Side Analysis
      • 2.3.3.1. Availability
      • 2.3.3.2. Infrastructure
      • 2.3.3.3. Regulatory Environment
      • 2.3.3.4. Market Competition
      • 2.3.3.5. Economic Viability (Consumer's Perspective)
    • 2.3.4. Demand Side Analysis
      • 2.3.4.1. Regulatory frameworks
      • 2.3.4.2. Technological Advancements
      • 2.3.4.3. Environmental Considerations
      • 2.3.4.4. Consumer Awareness & Acceptance
  • 2.4. Estimation Methodology
  • 2.5. Years Considered for the Study
  • 2.6. Currency Conversion Rates

Chapter 3. Global AI in the Oil and Gas Market Dynamics

  • 3.1. Market Drivers
    • 3.1.1. Increasing Demand for Operational Efficiency
    • 3.1.2. Safety Enhancement and Hazard Prevention
    • 3.1.3. Cost Reduction Initiatives
  • 3.2. Market Challenges
    • 3.2.1. Data Quality and Availability Issues
    • 3.2.2. Complexity Across the Value Chain
  • 3.3. Market Opportunities
    • 3.3.1. AI in Exploration and Production
    • 3.3.2. Predictive Maintenance and Downtime Reduction
    • 3.3.3. Advanced AI Applications in Safety

Chapter 4. Global AI in the Oil and Gas Market Industry Analysis

  • 4.1. Porter's 5 Force Model
    • 4.1.1. Bargaining Power of Suppliers
    • 4.1.2. Bargaining Power of Buyers
    • 4.1.3. Threat of New Entrants
    • 4.1.4. Threat of Substitutes
    • 4.1.5. Competitive Rivalry
    • 4.1.6. Futuristic Approach to Porter's 5 Force Model
    • 4.1.7. Porter's 5 Force Impact Analysis
  • 4.2. PESTEL Analysis
    • 4.2.1. Political
    • 4.2.2. Economical
    • 4.2.3. Social
    • 4.2.4. Technological
    • 4.2.5. Environmental
    • 4.2.6. Legal
  • 4.3. Top investment opportunity
  • 4.4. Top winning strategies
  • 4.5. Disruptive Trends
  • 4.6. Industry Expert Perspective
  • 4.7. Analyst Recommendation & Conclusion

Chapter 5. Global AI in the Oil and Gas Market Size & Forecasts by Component 2022-2032

  • 5.1. Segment Dashboard
  • 5.2. Global AI in the Oil and Gas Market: Component Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 5.2.1. Solution
    • 5.2.2. Services

Chapter 6. Global AI in the Oil and Gas Market Size & Forecasts by Operation 2022-2032

  • 6.1. Segment Dashboard
  • 6.2. Global AI in the Oil and Gas Market: Operation Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 6.2.1. Upstream
    • 6.2.2. Midstream
    • 6.2.3. Downstream

Chapter 7. Global AI in the Oil and Gas Market Size & Forecasts by Region 2022-2032

  • 7.1. North America AI in the Oil and Gas Market
    • 7.1.1. U.S. AI in the Oil and Gas Market
      • 7.1.1.1. Component breakdown size & forecasts, 2022-2032
      • 7.1.1.2. Operation breakdown size & forecasts, 2022-2032
    • 7.1.2. Canada AI in the Oil and Gas Market
  • 7.2. Europe AI in the Oil and Gas Market
    • 7.2.1. U.K. AI in the Oil and Gas Market
    • 7.2.2. Germany AI in the Oil and Gas Market
    • 7.2.3. France AI in the Oil and Gas Market
    • 7.2.4. Spain AI in the Oil and Gas Market
    • 7.2.5. Italy AI in the Oil and Gas Market
    • 7.2.6. Rest of Europe AI in the Oil and Gas Market
  • 7.3. Asia-Pacific AI in the Oil and Gas Market
    • 7.3.1. China AI in the Oil and Gas Market
    • 7.3.2. India AI in the Oil and Gas Market
    • 7.3.3. Japan AI in the Oil and Gas Market
    • 7.3.4. Australia AI in the Oil and Gas Market
    • 7.3.5. South Korea AI in the Oil and Gas Market
    • 7.3.6. Rest of Asia Pacific AI in the Oil and Gas Market
  • 7.4. Latin America AI in the Oil and Gas Market
    • 7.4.1. Brazil AI in the Oil and Gas Market
    • 7.4.2. Mexico AI in the Oil and Gas Market
    • 7.4.3. Rest of Latin America AI in the Oil and Gas Market
  • 7.5. Middle East & Africa AI in the Oil and Gas Market
    • 7.5.1. Saudi Arabia AI in the Oil and Gas Market
    • 7.5.2. South Africa AI in the Oil and Gas Market
    • 7.5.3. Rest of Middle East & Africa AI in the Oil and Gas Market

Chapter 8. Competitive Intelligence

  • 8.1. Key Company SWOT Analysis
  • 8.2. Top Market Strategies
  • 8.3. Company Profiles
    • 8.3.1. Baker Hughes
      • 8.3.1.1. Key Information
      • 8.3.1.2. Overview
      • 8.3.1.3. Financial (Subject to Data Availability)
      • 8.3.1.4. Product Summary
      • 8.3.1.5. Market Strategies
    • 8.3.2. Microsoft
    • 8.3.3. C3.ai
    • 8.3.4. Siemens
    • 8.3.5. Honeywell
    • 8.3.6. Oracle
    • 8.3.7. Accenture
    • 8.3.8. Google Cloud
    • 8.3.9. Rockwell Automation
    • 8.3.10. Infosys
    • 8.3.11. TIBCO Software
    • 8.3.12. ABB
    • 8.3.13. IBM
    • 8.3.14. Schlumberger
    • 8.3.15. Halliburton

Chapter 9. Research Process

  • 9.1. Research Process
    • 9.1.1. Data Mining
    • 9.1.2. Analysis
    • 9.1.3. Market Estimation
    • 9.1.4. Validation
    • 9.1.5. Publishing
  • 9.2. Research Attributes