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市場調查報告書
商品編碼
1527280

全球石油和天然氣人工智慧市場規模研究，按組成部分（解決方案、服務）、營運（上游、中游、下游）和 2022-2032 年區域預測

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個工作天內

價格
簡介目錄

預計到 2023 年，全球人工智慧在石油和天然氣市場的價值約為 35 億美元，預計 2024 年至 2032 年將以 14.1% 的年複合成長率(CAGR) 成長，最終達到 130 億美元的估值到2032年底。它可以比傳統方法更準確、更快速地分析地震資料來識別潛在的石油和天然氣儲量。這種能力使公司能夠就鑽井和資源開採做出更好的決策。此外，人工智慧透過分析感測器資料來預測設備故障，從而協助預測性維護，從而減少停機時間、維護成本並提高安全性。

石油和天然氣產業擴大採用人工智慧（AI），主要是出於提高營運效率的需要。人工智慧驅動的系統分析來自感測器和其他來源的資料，以識別效率低下的情況，使公司能夠採取糾正措施。此外，人工智慧在識別潛在安全隱患方面發揮著至關重要的作用，可以採取主動措施來防止事故和傷害。透過最佳化營運和識別效率低下的情況，人工智慧可以幫助企業在競爭激烈的市場中降低營運成本並提高獲利能力。此外，石油和天然氣產業擴大採用人工智慧（AI）主要是出於提高營運效率的需要。人工智慧驅動的系統分析來自感測器和其他來源的資料，以識別效率低下的情況，使公司能夠採取糾正措施。此外，人工智慧在識別潛在安全隱患方面發揮著至關重要的作用，可以採取主動措施來防止事故和傷害。透過最佳化營運和識別低效率，人工智慧可以幫助企業在競爭激烈的市場中降低營運成本並提高獲利能力。儘管有這些好處，但資料品質和可用性等挑戰仍然存在。高品質的資料對於人工智慧演算法的有效運作至關重要。石油和天然氣產業歷來面臨資料孤島、資料集不完整和缺乏標準化的問題，使得人工智慧模型很難在整個價值鏈上發揮作用。

全球石油和天然氣人工智慧市場研究涵蓋的關鍵區域包括亞太地區、北美、歐洲、拉丁美洲和世界其他地區。北美是石油和天然氣領域人工智慧的領先市場，其推動因素包括其強勁的經濟、人工智慧技術的廣泛採用、頂級人工智慧軟體和系統供應商的大量存在，以及政府和私人實體在研發方面的聯合投資。該地區石油和天然氣生產能力的擴大和投資的增加預計將進一步增加市場機會。

目錄

第 1 章：石油和天然氣市場中的全球人工智慧執行摘要

  • 全球人工智慧在石油和天然氣市場的規模及預測（2022-2032）
  • 區域概要
  • 分部摘要
    • 按組件
    • 按操作
  • 主要趨勢
  • 經濟衰退的影響
  • 分析師推薦與結論

第 2 章：全球人工智慧在石油和天然氣市場的定義和研究假設

  • 研究目的
  • 市場定義
  • 研究假設
    • 包容與排除
    • 限制
    • 供給側分析
      • 可用性
      • 基礎設施
      • 監管環境
      • 市場競爭
      • 經濟可行性（消費者的角度）
    • 需求面分析
      • 監理框架
      • 技術進步
      • 環境考慮
      • 消費者意識和接受度
  • 估算方法
  • 研究涵蓋的年份
  • 貨幣兌換率

第 3 章：全球人工智慧在石油和天然氣市場動態中的應用

  • 市場促進因素
    • 對營運效率的需求不斷增加
    • 安全增強和危險預防
    • 降低成本舉措
  • 市場挑戰
    • 數據品質和可用性問題
    • 整個價值鏈的複雜性
  • 市場機會
    • 探勘和生產中的人工智慧
    • 預測性維護和減少停機時間
    • 安全領域的先進人工智慧應用

第 4 章：全球人工智慧在石油和天然氣市場的產業分析

  • 波特的五力模型
    • 供應商的議價能力
    • 買家的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭競爭
    • 波特五力模型的未來方法
    • 波特的 5 力影響分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社會的
    • 技術性
    • 環境的
    • 合法的
  • 頂級投資機會
  • 最佳制勝策略
  • 顛覆性趨勢
  • 產業專家視角
  • 分析師推薦與結論

第 5 章：全球人工智慧在石油和天然氣市場的規模和預測：按組成部分 - 2022-2032

  • 細分儀表板
  • 石油和天然氣市場中的全球人工智慧：2022 年和 2032 年組件收入趨勢分析
    • 解決方案
    • 服務

第 6 章：全球人工智慧在石油和天然氣市場的規模和預測：按營運分類 - 2022-2032

  • 細分儀表板
  • 全球石油和天然氣市場人工智慧：2022年和2032年營運收入趨勢分析
    • 上游
    • 中游
    • 下游

第 7 章：全球人工智慧在石油和天然氣市場的規模和預測：按地區 - 2022-2032

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 西班牙
    • 義大利
    • 歐洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 亞太地區其他地區
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 南非
    • 中東和非洲其他地區

第 8 章：競爭情報

  • 重點企業SWOT分析
  • 頂級市場策略
  • 公司簡介
    • Baker Hughes
      • 關鍵訊息
      • 概述
      • 財務（視數據可用性而定）
      • 產品概要
      • 市場策略
    • Microsoft
    • C3.ai
    • Siemens
    • Honeywell
    • Oracle
    • Accenture
    • Google Cloud
    • Rockwell Automation
    • Infosys
    • TIBCO Software
    • ABB
    • IBM
    • Schlumberger
    • Halliburton

第 9 章：研究過程

  • 研究過程
    • 資料探勘
    • 分析
    • 市場預測
    • 驗證
    • 出版
  • 研究屬性
簡介目錄

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