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1863659

全球油氣數位孿生市場：依類型、應用、部署模式和公司規模劃分 - 市場規模、行業趨勢、機會分析和預測（2025-2033 年）

Global Digital Twin in Oil & Gas Market: By Type, Application, Deployment, Enterprise Size - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2025-2033
出版日期: | 出版商: Astute Analytica | 英文 234 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

油氣數位孿生市場正經歷快速成長，反映出該產業越來越依賴先進的數位技術來優化複雜的營運。 2024 年，該市場規模約為 1.3672 億美元，預計將顯著擴張，到 2033 年達到 11.3732 億美元。這一令人矚目的成長意味著 2025 年至 2033 年的複合年增長率 (CAGR) 為 26.54%。這種強勁的成長表明，在最具挑戰性的工業環境之一中，對創新解決方案的需求不斷增長，以提高營運效率、提升安全標準並實現預測性維護。

該市場發展的核心是創建先進的虛擬模型，以複製鑽井平台、管道和煉油廠等實體資產。這些數位孿生體作為動態表示，會根據嵌入資產的廣泛感測器網路收集的即時數據不斷更新。人工智慧 (AI) 的整合透過實現進階分析、模式識別和預測洞察，進一步提升了這些模型的價值。

市場動態

石油和天然氣數位孿生市場的主要參與者包括 IBM、西門子和 AVEVA 等知名科技公司，它們為該技術的廣泛應用和發展做出了重大貢獻。隨著企業從有限的試點計畫轉向全面、全企業範圍地採用數位孿生解決方案，這些公司正在見證產業行為的決定性轉變。

2025 年 11 月，華為及其合作夥伴宣布推出一項聯合解決方案，旨在促進石油和天然氣作業的智慧化，這是一項重要的進展。此次合作的重要參與者之一是中國石油天然氣集團公司 (CNPC) 旗下的地球物理勘探公司 BGP。兩家公司共同向世界展示了其在油氣勘探領域的成就，重點介紹了整合數位孿生技術在變革勘探活動和營運流程方面的潛力。

同時，橫河電機株式會社旗下公司KBC於2025年8月發布了其旗艦數位孿生流程模擬平台 "Petro-SIM® v7.6" 的最新版本。更新後的平台支援油氣產業的上游和下游領域，涵蓋煉油、石化、聚合物生產以及永續航空燃料（SAF）等新興領域。

核心成長驅動因子

推動油氣市場對數位孿生技術需求的關鍵因素是該行業對降低勘探活動中地下不確定性的強烈需求。這一領域的風險極高，深水鑽井作業中一口乾井的成本可能超過1.5億美元。如此龐大的成本使得人們迫切需要更精確、更可靠的地質模型，以顯著降低鑽井失敗的風險。為了應對這項挑戰，各公司正越來越多地轉向下一代地下數位孿生技術，這代表著勘探技術的重大飛躍。

新機遇

將量子運算啟發的方法應用於數位孿生技術中高度複雜的最佳化問題，正在創造巨大的機會。傳統的計算方法往往難以處理某些油氣製程中固有的大量變數和複雜計算。然而，受量子啟發的演算法提供了一個很有前景的解決方案，使其能夠有效率地處理傳統電腦難以即時解決的複雜問題。例如，煉油廠的催化裂解製程需要同時優化數千個相互依存的變量，以最大限度地提高效率和產量。

優化障礙

將數位孿生技術與現有傳統營運系統整合的複雜性是一個重大挑戰，可能會阻礙數位孿生市場的成長。許多油氣公司仍然依賴過時的基礎設施和軟體平台，這些平台並非為支援先進的數位技術而設計。這為數位孿生的實施帶來了巨大的技術障礙。新系統必須與各種各樣的舊式硬體和軟體解決方案相容並有效通訊——這個過程通常需要大量的客製化、資料遷移和系統升級，既耗時又昂貴。

目錄

第一章：研究架構

  • 研究目標
  • 產品概述
  • 市場區隔

第二章：研究方法

  • 質性研究
    • 一手和二手資料來源
  • 量化研究
    • 一手和二手資料來源
  • 依地區劃分的一手調查受訪者組成
  • 研究假設
  • 市場規模估算
  • 資料三角驗證

第三章：摘要整理：全球石油天然氣數位孿生市場

第四章：全球石油天然氣數位孿生市場概論天然氣

  • 產業價值鏈分析
    • 開發商
    • 技術整合商
    • 服務提供者
    • 公司規模
  • 行業展望
    • 數位孿生技術在石油和天然氣產業的影響
    • 數位孿生技術在海上平台上的應用
  • PESTLE 分析
  • 波特五力分析
    • 供應商議價能力
    • 買方議價能力
    • 替代品威脅
    • 新進入者威脅
    • 競爭強度
  • 市場動態與趨勢
    • 成長推動因素
    • 阻礙因素
    • 機遇
    • 主要趨勢
  • COVID-19 對市場成長趨勢的影響評估
  • 市場成長與展望
    • 市場收入估計與預測（2020-2033 年）
    • 價格分析
  • 競爭格局概覽
    • 市場集中度
    • 公司市佔率分析（價值，2024 年）
    • 競爭格局圖

第五章 全球石油天然氣產業數位孿生市場（依類型劃分）

  • 主要發現
  • 市場規模與預測（2020-2033 年）
    • 描述性孿生
    • 資訊性孿生
    • 預測性孿生
    • 綜合性孿生
    • 自主型孿生數位孿生

第六章：全球油氣產業數位孿生市場（依組件劃分）

  • 主要發現
  • 市場規模及預測（2020-2033 年）
    • 產品數位孿生
    • 流程數位孿生
    • 系統數位孿生

    第七章：全球油氣產業數位孿生市場（依應用劃分）

    • 主要洞察
    • 市場規模及預測（2020-2033 年）
    • 鑽井
    • 緊急疏散
    • 管道
    • 智慧油田
    • 虛擬學習與訓練
    • 資產監控與維護
    • 專案規劃與生命週期管理
    • 協作與知識分享
    • 海上平台和基礎設施
    • 勘探和地質調查

第八章：全球石油天然氣數位孿生市場（以部署模式劃分）

  • 主要見解
  • 市場規模及預測（2020-2033 年）
    • 雲端部署
    • 本地部署

第九章：全球石油天然氣數位孿生市場（依公司規模劃分）

  • 主要發現
  • 市場規模及預測（2020-2033 年）
  • 大型企業
  • 中小企業 (SME)

第十章：全球石油天然氣數位孿生市場分析（依…劃分）區域

  • 主要發現
  • 市場規模及預測，2020–2033
    • 北美
    • 歐洲
    • 亞太地區
    • 中東和非洲 (MEA)
    • 南美

第11章：北美油氣數位孿生市場分析

第12章：歐洲油氣數位孿生市場分析

第13章：亞太油氣數位孿生市場分析

第14章：中東與非洲油氣數位孿生市場分析

第15章：南美油氣數位孿生市場分析

第16章：英國油氣數位孿生市場分析

第17章：德國油氣數位孿生市場分析

第18章：義大利油氣數位孿生市場分析

第19章：西班牙油氣數位孿生市場分析

第20章：法國油氣數位孿生市場分析

第21章：波蘭油氣數位孿生市場分析

第22章：俄羅斯油氣數位孿生市場分析

第23章 企業簡介

  • Ansys, Inc.
  • General Electric
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • SWIM.AI
  • Other prominent players
簡介目錄
Product Code: AA1023630

The digital twin market within the oil and gas industry is experiencing rapid growth, reflecting the sector's increasing reliance on advanced digital technologies to optimize complex operations. Valued at approximately US$ 136.72 million in 2024, this market is projected to expand significantly, reaching an estimated valuation of US$ 1,137.32 million by 2033. This impressive expansion corresponds to a compound annual growth rate (CAGR) of 26.54% during the forecast period from 2025 to 2033. Such robust growth highlights the escalating demand for innovative solutions that enhance operational efficiency, improve safety standards, and enable predictive maintenance in one of the most challenging industrial environments.

At the core of this market's development is the creation of sophisticated virtual models that replicate physical assets, including drilling rigs, pipelines, and refineries. These digital twins serve as dynamic representations that are continuously updated with real-time data collected from a wide network of sensors embedded in the equipment. The integration of artificial intelligence further enhances the value of these models by enabling advanced analytics, pattern recognition, and predictive insights.

Noteworthy Market Developments

Key players in the digital twin in oil and gas market include prominent technology companies such as IBM, Siemens, and AVEVA, which have been instrumental in advancing the adoption and capabilities of this technology. These companies are witnessing a decisive shift in industry behavior, as organizations move beyond limited pilot programs to embrace comprehensive, enterprise-wide deployments of digital twin solutions.

In November 2025, a significant development occurred with Huawei and its partners launching joint solutions aimed at promoting intelligent oil and gas operations. One notable participant in this collaboration was BGP, a geophysical exploration specialist operating under the China National Petroleum Corporation (CNPC). Together, they showcased their achievements in oil and gas exploration to a global audience, highlighting the potential of integrated digital twin technologies to transform exploration efforts and operational workflows.

In a related advancement, August 2025 KBC, a Yokogawa Company, announced the release of Petro-SIM(R) v7.6, the latest version of its flagship digital twin process simulation platform. This updated platform caters to both upstream and downstream sectors of the oil and gas industry, encompassing refining, petrochemical, polymer production, and emerging areas such as sustainable aviation fuel (SAF).

Core Growth Drivers

A primary driver of demand in the digital twin in oil and gas market is the industry's intense focus on reducing subsurface uncertainty during exploration activities. The stakes are incredibly high in this area, as the cost of drilling a single deepwater dry hole can surpass 150 million dollars. Such enormous expenses create an urgent imperative for more precise and reliable geological models that can significantly reduce the risk of unsuccessful drilling. To meet this challenge, companies are increasingly turning to next-generation subsurface digital twins, which represent a sophisticated leap forward in exploration technology.

Emerging Opportunity Trends

A significant opportunity is arising from the application of quantum-inspired computing to address highly complex optimization challenges within digital twin technology. Traditional computational methods often struggle to handle the enormous variables and intricate calculations involved in certain oil and gas processes. Quantum-inspired algorithms, however, provide a promising solution by enabling the efficient processing of problems that are otherwise too complex for classical computers to solve in real time. For example, in refinery operations, catalytic cracking processes involve thousands of interdependent variables that must be optimized simultaneously to maximize efficiency and output.

Barriers to Optimization

The complexity involved in integrating digital twin technology with existing legacy operational systems presents a significant challenge that may hinder the growth of the digital twin market. Many oil and gas companies still rely on older infrastructure and software platforms that were not originally designed to support advanced digital technologies. This creates substantial technical barriers when attempting to implement digital twins, as the new systems must be compatible with, and able to effectively communicate with, a wide range of outdated hardware and software solutions. The process often requires extensive customization, data migration, and system upgrades, which can be both time-consuming and costly.

Detailed Market Segmentation

By Type, the informative twin segment stands out within the global market, securing an impressive 27% share. This dominance is largely driven by the segment's ability to transform vast volumes of raw data into actionable intelligence, which is critical for optimizing operations and decision-making in a complex industry. Informative twins serve as sophisticated digital replicas that go beyond mere visualization, offering a comprehensive and contextualized view of both assets and overall operational processes.

By Component, the process digital twin segment commands a leading position in the global market, accounting for over 46% of the revenue. This segment distinguishes itself by enabling companies to simulate and optimize entire operational workflows rather than focusing on individual assets. By creating comprehensive virtual models of complex, interconnected systems, process digital twins provide a holistic view of critical industry operations such as drilling activities, refining processes, or complete liquefied natural gas (LNG) production chains. This broader perspective allows operators to analyze how different components interact and influence overall performance, which is essential for improving efficiency and reducing operational risks.

By Application, the asset monitoring and maintenance segment holds a prominent position in the market, capturing over 19% of the total market share. This segment addresses one of the most pressing challenges faced by the industry: unplanned downtime, which can result in costly disruptions, safety risks, and operational inefficiencies. By leveraging digital twin technology, companies create precise virtual replicas of critical equipment such as pumps, turbines, and pipelines. These digital models are continuously fed with real-time sensor data, allowing for constant monitoring of the equipment's health and performance.

By Deployment, the cloud segment has emerged as the undisputed leader in the market, commanding an overwhelming market share of more than 70.9%. This dominance is largely attributed to the cloud's inherent scalability, which allows companies to easily expand or reduce their digital twin operations based on fluctuating demands. The flexibility offered by cloud platforms is particularly valuable in the oil and gas industry, where operational scales can vary dramatically and projects often require rapid deployment of advanced technologies across geographically dispersed sites.

Segment Breakdown

By Type

  • Descriptive twin
  • Informative twin
  • Predictive twin
  • Comprehensive twin
  • Autonomous twin

By Application

  • Drilling
  • Emergency evacuation
  • Pipelines
  • Intelligent Oil Fields
  • Virtual Learning and Training
  • Asset Monitoring and Maintenance
  • Project Planning and lifecycle management
  • Collaboration and knowledge sharing
  • Offshore platforms and infrastructure
  • Exploration and geological study

By Component

  • Product Digital Twin
  • Process Digital Twin
  • System Digital Twin

By Deployment

  • On-Premise
  • Cloud

By Enterprise Size

  • Large Enterprises
  • Small and Medium-sized Enterprises (SMEs)

By Region

  • North America
  • The US
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Australia & New Zealand
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA
  • South America
  • Brazil
  • Argentina
  • Rest of South America

Geography Breakdown

  • North America holds a commanding position in the digital twin market for the oil and gas sector, capturing a significant share of over 32.80%. This leadership is largely driven by the extensive deployment of digital twin technologies in the region's prolific shale plays, which are among the most active and technologically advanced oil production areas in the world. A prime example is the Permian Basin, where Chevron is pioneering its "digital factory" initiative.
  • The impact of digital twin adoption is further exemplified by a major operator in the region that processes over 20 terabytes of production data daily from its shale operations. This vast amount of data is analyzed to improve efficiency, predict equipment failures, and enhance overall operational decision-making. Supporting this technological advancement is Houston's vibrant tech ecosystem, which plays a crucial role as an enabler of innovation in the oil and gas digital twin space.

Leading Market Participants

  • Ansys, Inc.
  • General Electric
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • SWIM.AI
  • Other prominent players

Table of Content

Chapter 1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

Chapter 2. Research Methodology

  • 2.1. Qualitative Research
    • 2.1.1. Primary & Secondary Sources
  • 2.2. Quantitative Research
    • 2.2.1. Primary & Secondary Sources
  • 2.3. Breakdown of Primary Research Respondents, By Region
  • 2.4. Assumption for the Study
  • 2.5. Market Size Estimation
  • 2.6. Data Triangulation

Chapter 3. Executive Summary: Global Digital Twin in Oil & Gas Market

Chapter 4. Global Digital Twin in Oil & Gas Market Overview

  • 4.1. Industry Value Chain Analysis
    • 4.1.1. Developer
    • 4.1.2. Technology Integrator
    • 4.1.3. Service Provider
    • 4.1.4. Enterprise Size
  • 4.2. Industry Outlook
    • 4.2.1. Impact of digital twins in Oil & Gas
    • 4.2.2. Offshore Platform in Digital Twins
  • 4.3. PESTLE Analysis
  • 4.4. Porter's Five Forces Analysis
    • 4.4.1. Bargaining Power of Suppliers
    • 4.4.2. Bargaining Power of Buyers
    • 4.4.3. Threat of Substitutes
    • 4.4.4. Threat of New Entrants
    • 4.4.5. Degree of Competition
  • 4.5. Market Dynamics and Trends
    • 4.5.1. Growth Drivers
    • 4.5.2. Restraints
    • 4.5.3. Opportunities
    • 4.5.4. Key Trends
  • 4.6. Covid-19 Impact Assessment on Market Growth Trend
  • 4.7. Market Growth and Outlook
    • 4.7.1. Market Revenue Estimates and Forecast (US$ Mn), 2020 - 2033
    • 4.7.2. Pricing Analysis
  • 4.8. Competition Dashboard
    • 4.8.1. Market Concentration Rate
    • 4.8.2. Company Market Share Analysis (Value %), 2024
    • 4.8.3. Competitor Mapping

Chapter 5. Global Digital Twin in Oil & Gas Market, By Type

  • 5.1. Key Insights
  • 5.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 5.2.1. Descriptive twin
    • 5.2.2. Informative twin
    • 5.2.3. Predictive twin
    • 5.2.4. Comprehensive twin
    • 5.2.5. Autonomous twin

Chapter 6. Global Digital Twin in Oil & Gas Market, By Component

  • 6.1. Key Insights
  • 6.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 6.2.1. Product Digital Twin
    • 6.2.2. Process Digital Twin
    • 6.2.3. System Digital Twin

Chapter 7. Global Digital Twin in Oil & Gas Market, By Application

  • 7.1. Key Insights
  • 7.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 7.2.1. Drilling
    • 7.2.2. Emergency evacuation
    • 7.2.3. Pipelines
    • 7.2.4. Intelligent Oil fields
    • 7.2.5. Virtual Learning and Training
    • 7.2.6. Asset Monitoring and Maintenance
    • 7.2.7. Project Planning and lifecycle management
    • 7.2.8. Collaboration and knowledge sharing
    • 7.2.9. Offshore platforms and infrastructure
    • 7.2.10. Exploration and geological study

Chapter 8. Global Digital Twin in Oil & Gas Market, By Deployment

  • 8.1. Key Insights
  • 8.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 8.2.1. Cloud based
    • 8.2.2. On-Premises

Chapter 9. Global Digital Twin in Oil & Gas Market, By Enterprise Size

  • 9.1. Key Insights
  • 9.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 9.2.1. Large Enterprises
    • 9.2.2. Small and Medium-sized Enterprises (SMEs)

Chapter 10. Global Digital Twin in Oil & Gas Market Analysis, By Region

  • 10.1. Key Insights
  • 10.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 10.2.1. North America
      • 10.2.1.1. The U.S.
      • 10.2.1.2. Canada
      • 10.2.1.3. Mexico
    • 10.2.2. Europe
      • 10.2.2.1. Western Europe
        • 10.2.2.1.1. The UK
        • 10.2.2.1.2. Germany
        • 10.2.2.1.3. France
        • 10.2.2.1.4. Italy
        • 10.2.2.1.5. Spain
        • 10.2.2.1.6. Rest of Western Europe
      • 10.2.2.2. Eastern Europe
        • 10.2.2.2.1. Poland
        • 10.2.2.2.2. Russia
        • 10.2.2.2.3. Rest of Eastern Europe
    • 10.2.3. Asia Pacific
      • 10.2.3.1. China
      • 10.2.3.2. India
      • 10.2.3.3. Japan
      • 10.2.3.4. South Korea
      • 10.2.3.5. Australia & New Zealand
      • 10.2.3.6. ASEAN
      • 10.2.3.7. Rest of Asia Pacific
    • 10.2.4. Middle East & Africa (MEA)
      • 10.2.4.1. UAE
      • 10.2.4.2. Saudi Arabia
      • 10.2.4.3. South Africa
      • 10.2.4.4. Rest of MEA
    • 10.2.5. South America
      • 10.2.5.1. Brazil
      • 10.2.5.2. Argentina
      • 10.2.5.3. Rest of South America

Chapter 11. North America Digital Twin in Oil & Gas Market Analysis

  • 11.1. Key Insights
  • 11.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 11.2.1. By Type
    • 11.2.2. By Component
    • 11.2.3. By Application
    • 11.2.4. By Deployment
    • 11.2.5. By Enterprise Size
    • 11.2.6. By Country

Chapter 12. Europe Digital Twin in Oil & Gas Market Analysis

  • 12.1. Key Insights
  • 12.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 12.2.1. By Type
    • 12.2.2. By Component
    • 12.2.3. By Application
    • 12.2.4. By Deployment
    • 12.2.5. By Enterprise Size
    • 12.2.6. By Country

Chapter 13. Asia Pacific Digital Twin in Oil & Gas Market Analysis

  • 13.1. Key Insights
  • 13.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 13.2.1. By Type
    • 13.2.2. By Component
    • 13.2.3. By Application
    • 13.2.4. By Deployment
    • 13.2.5. By Enterprise Size
    • 13.2.6. By Country

Chapter 14. Middle East & Africa Digital Twin in Oil & Gas Market Analysis

  • 14.1. Key Insights
  • 14.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 14.2.1. By Type
    • 14.2.2. By Component
    • 14.2.3. By Application
    • 14.2.4. By Deployment
    • 14.2.5. By Enterprise Size
    • 14.2.6. By Country

Chapter 15. South America Digital Twin in Oil & Gas Market Analysis

  • 15.1. Key Insights
  • 15.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 15.2.1. By Type
    • 15.2.2. By Component
    • 15.2.3. By Application
    • 15.2.4. By Deployment
    • 15.2.5. By Enterprise Size
    • 15.2.6. By Country

Chapter 16. The UK Digital Twin in Oil & Gas Market Analysis

  • 16.1. Key Insights
  • 16.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 16.2.1. By Type
    • 16.2.2. By Component
    • 16.2.3. By Application
    • 16.2.4. By Deployment
    • 16.2.5. By Enterprise Size

Chapter 17. Germany Digital Twin in Oil & Gas Market Analysis

  • 17.1. Key Insights
  • 17.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 17.2.1. By Type
    • 17.2.2. By Component
    • 17.2.3. By Application
    • 17.2.4. By Deployment
    • 17.2.5. By Enterprise Size

Chapter 18. Italy Digital Twin in Oil & Gas Market Analysis

  • 18.1. Key Insights
  • 18.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 18.2.1. By Type
    • 18.2.2. By Component
    • 18.2.3. By Application
    • 18.2.4. By Deployment
    • 18.2.5. By Enterprise Size

Chapter 19. Spain Digital Twin in Oil & Gas Market Analysis

  • 19.1. Key Insights
  • 19.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 19.2.1. By Type
    • 19.2.2. By Component
    • 19.2.3. By Application
    • 19.2.4. By Deployment
    • 19.2.5. By Enterprise Size

Chapter 20. France Digital Twin in Oil & Gas Market Analysis

  • 20.1. Key Insights
  • 20.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 20.2.1. By Type
    • 20.2.2. By Component
    • 20.2.3. By Application
    • 20.2.4. By Deployment
    • 20.2.5. By Enterprise Size

Chapter 21. Poland Digital Twin in Oil & Gas Market Analysis

  • 21.1. Key Insights
  • 21.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 21.2.1. By Type
    • 21.2.2. By Component
    • 21.2.3. By Application
    • 21.2.4. By Deployment
    • 21.2.5. By Enterprise Size

Chapter 22. Russia Digital Twin in Oil & Gas Market Analysis

  • 22.1. Key Insights
  • 22.2. Market Size and Forecast, 2020 - 2033 (US$ Mn)
    • 22.2.1. By Type
    • 22.2.2. By Component
    • 22.2.3. By Application
    • 22.2.4. By Deployment
    • 22.2.5. By Enterprise Size

Chapter 23. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, Measurement Methods, and Business Strategy Outlook)

  • 23.1. Ansys, Inc.
  • 23.2. General Electric
  • 23.3. IBM Corporation
  • 23.4. Microsoft Corporation
  • 23.5. Oracle Corporation
  • 23.6. PTC Inc.
  • 23.7. Robert Bosch GmbH
  • 23.8. SAP SE
  • 23.9. Siemens AG
  • 23.10. SWIM.AI
  • 23.11. Other prominent players