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市場調查報告書

匯流帶來到油田設施的影響

Convergence Impact on Oilfield Services

出版商 Frost & Sullivan 商品編碼 357211
出版日期 內容資訊 英文 114 Pages
商品交期: 最快1-2個工作天內
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匯流帶來到油田設施的影響 Convergence Impact on Oilfield Services
出版日期: 2016年04月29日 內容資訊: 英文 114 Pages
簡介

隨著石油價格的下滑,淨利率的減少,各組織被迫透過先進技術來以解決方案取代非效率順序。連網型(連接)油田設施,便成為縮減開採期間,提高生產率的一大助力。轉換到數位後,事業的重心由傳統的追求生產量轉換到追求效率。銷售設備的商務模式也被以性能為訴求的商務模式所取代,自動化組合中分析的作用也日益重要。

本報告提供匯流對油田設施(OFS)市場的影響調查分析,概括產業性弱點這個商務課題,加上具體的案例,解說技術匯流的結果──連網型油田設施的興起之特徵,同時概括今後促進引進的分析,AI(人工智能)的新經營模式相關分析。

第1章 摘要整理

第2章 從大趨勢到MICRO BOOMS與:定義關係性

  • 從大趨勢到MICRO BOOMS
  • 大趨勢的匯流
  • MICRO BOOMS‐可配合的洞察組成架構

第3章 簡介‐調查範圍與區分

  • 調查範圍
  • 產品定義

第4章 第一階段‐商務課題

第5章 闡明OFS(油田設施)市場上非效率是產業的弱點,資產運轉率減少20%

  • 油田設施‐定義
  • 弱點1‐資產的可用性
  • 弱點2‐核心服務的高成本
  • 弱點3‐產業淨利率的保護
  • 油田設施‐結構上的轉換
  • 智慧油田設施
  • 生態系統‐人名錄

第6章 第二階段‐成長機會

  • 成長機會

第7章 不穩定的石油價格和促進產業效率的需求促進高反應的連網型油田設施的需求

  • 技術匯流趨勢勝過非效率
  • 定義連網型油田設施‐現實
  • 連網型油田設施‐深架構與廣的結構
  • 石油、天然氣產業所面臨的課題
  • 多級IT-OT架構
  • 完全被整合化的數位油田
  • 成長機會
  • 促進智慧油田的結果‐處理效率
  • 推進油田的效率的夥伴關係
  • 使用案例1‐生產效率:BP + GE Predix
  • 連網型油田設施對預測性資產管理的影響
  • 使用案例2‐預測性資產管理:IBM Smart Solutions
  • 從細孔到管線組合的必要性
  • 使用案例3‐資產的性能:Schlumberger + Cameron
  • 連網型油田設施對生產成本效率的影響
  • 使用案例4‐生產成本效率:SAP SE + Mobility

第8章 第三階段‐策略與洞察

  • 策略與洞察

第9章 油田設施數位平台的出現破壞、崩壞及轉換了分散化的油田產業

  • 促使OFS平台‐整合的弱點
  • 數位油田的結構要素‐將隔離的各部分合而為一
  • 平台引進的影響
  • 相關利益者的平台
  • 單一平台‐新必要性
  • 生態系統‐人名指南
  • 生態系統‐合併·收購
  • 使用案例1‐GE:Full串流服務供應商的地位
  • 使用案例2‐Halliburton:為了優化復甦的最佳化,有效利用雲端服務

第10章 2020年,50%OEM的收入由服務所得,成長主導的新模式取代了被破壞的舊經營模式

  • 智慧解決方案連接油田設施設備
  • 為了轉換油田產業‐經營模式的重要要素
  • 設備廠商經營模式的轉換
  • 設備廠商應該做什麼事?
  • 對OEM來說經營模式轉換的優點
  • 提供的資產類別及分析服務(部分清單)
  • 使用案例‐Chevron藉由預測性維護來監測性能

第11章 資本,資源及資產效益的未滿足需求威脅到自動化企業的生存並促使他們擴大組合

  • 自動化石油經營者的主要5個目的
  • 企業對石油經營者自動化的期待
  • 技術及專門領域‐把MAC變成MIMC
  • 自動化組合的擴大促進智慧商務
  • 油田自動化企業的M&A
  • 促進組合擴大的夥伴關係
  • 使用案例1‐核心自動化分析轉移
  • 案例2‐OFS中日漸形成新聯盟:分析PaaS
  • 使用案例3‐預測性分析可削減障礙

第12章 油田設施的ICT大大依賴AI;市場無人補助的時代到來

  • IT應用的發展‐AI繼承OFS
  • 油田設施的AI及ML的來到‐適時且破壞性的
  • 價值鏈整體與涉及的AI應用虛擬補助
  • 經營模式的轉換‐從雲端到AI
  • 人工智能‐新現實
  • 油田設施的人工智能架構
  • 神經系統‐在油田中更加堅固
  • 認知運算實現最佳化
  • 在油田設施中機器學習應用
  • 促進人工智能普及的夥伴關係
  • 使用案例1‐AI支援蘊藏量管理
  • 使用案例2‐複雜的現場生產最佳化
  • 使用案例3‐油田設施帶來AI的匯流
  • 使用案例4‐認知技術的Repsol-IBM聯盟
  • 使用案例5‐發現新技術境界的新油田
  • 人工智能與機器學習生態系統

第13章 主要建議

  • 主要的建議
  • 免責聲明

第14章 附錄

目錄
Product Code: NFE2-01-00-00-00

Fragmented Ecosystem Looks at Convergence Amid Volatile Oil Prices

As low oil prices give way to dwindling profit margins, strategies dictate organizations to curb inefficient practices and replace them with technologically advanced smart solutions. Connected oilfield services facilitate reduced drill time and increased productivity. The digital transformation has shifted focus to chasing efficiencies from the traditional one of chasing barrels. Business model disruptions to sell performance instead of equipment have taken over, as analytics becomes a strong part of the automation portfolio. The ICT industry has taken a leap of faith with the introduction of cognitive technologies through virtual assistants in oilfield services, taking artificial intelligence applications to a higher level.

Table of Contents

1. EXECUTIVE SUMMARY

  • Top 5 Things a CEO Should Know
  • Executive Summary
  • Oilfield Services Market-5 Industry Trends
  • Connected Oilfield Services Market-The Next Big Thing(s)
  • O&G Industry-Snapshot of Top Opportunities for AI
  • Convergence of People, Process, and Technology
  • Connected Oilfield Services-Game Changers

2. MEGA TRENDS TO MICRO BOOMS-DEFINITIONS AND TIE-INS

  • Mega Trends to Micro Booms
  • Convergence of Mega Trends
  • Micro Booms-Actionable Insights Framework

3. INTRODUCTION-SCOPE AND SEGMENTATION

  • Scope of the Study
  • Product Definitions
  • Product Definitions (continued)

4. FIRST BASE-BUSINESS ISSUES

  • Business Issues

5. INEFFICIENCIES IN THE OFS MARKET OPEN UP INDUSTRY PRESSURE POINTS, AS ASSET UTILIZATION RATE FALLS BY 20%

  • Oilfield Services-Definition
  • Oilfield Services-Definition (continued)
  • Pressure Point 1-Asset Availability
  • Pressure Point 1-Asset Availability (continued)
  • Pressure Point 2-High Costs of Core Services
  • Pressure Point 3-Protecting Operational Margins
  • Oilfield Services Market-Tectonic Shifts
  • Smart Oilfield Services
  • Ecosystem-The Who's Who

6. SECOND BASE-GROWTH OPPORTUNITIES

  • Growth Opportunities

7. VOLATILE OIL PRICES AND THE NEED TO DRIVE OPERATIONAL EFFICIENCY DRIVE THE DEMAND FOR RESPONSIVE AND CONNECTED OILFIELD SERVICES

  • Technology Convergence Trends to Drive Out Inefficiencies
  • Connected Oilfield Services-Defining Reality
  • Connected Oilfield Services-Deep Architecture & Wide Structure
  • Challenges Faced by the O&G Industry
  • Multilevel IT-OT Architecture
  • Totally Integrated Digital Oilfield
  • Growth Opportunities
  • Growth Opportunities (continued)
  • Outcomes of Smart Oilfields-Driving Throughput Efficiencies
  • Partnerships Driving Oilfield Efficiencies
  • Use Case 1-Production Efficiencies: BP + GE Predix
  • Connected Oilfield Services Impacting Predictive Asset Management
  • Use Case 2-Predictive Asset Management: IBM Smart Solutions
  • Need for Pore-to-pipeline Portfolio
  • Use Case 3-Asset Performance: Schlumberger + Cameron
  • Use Case 3-Asset Performance: Schlumberger + Cameron (continued)
  • Use Case 3-Asset Performance: Schlumberger + Cameron (continued)
  • Connected Oilfield Services Impacting Production Cost Efficiencies
  • Use Case 4-Production Cost Efficiencies: SAP SE + Mobility

8. THIRD BASE-STRATEGY AND INSIGHTS

  • Strategy and Insights

9. EMERGENCE OF A DIGITAL PLATFORM FOR OILFIELD SERVICES DISRUPTS, COLLAPSES, AND TRANSFORMS FRAGMENTED OILFIELD OPERATIONS

  • OFS Platforms-Pressure Points Driving Consolidation
  • Elements of Digital Oilfield-Bringing Together the Siloes
  • Key Factors Driving Innovation
  • Implications of Platform Adoption
  • Stakeholders Platform
  • Single Platform-The New Need
  • Ecosystem-The Who's Who
  • Ecosystem-Mergers and Acquisitions
  • Use Case 1-GE: Positioned as a Full Stream Service Provider
  • Use Case 1-GE: Positioned as a Full Stream Service Provider (continued)
  • Use Case 1-GE: Positioned as a Full Stream Service Provider (continued)
  • Use Case 2-Halliburton: Utilizing Cloud Services to Optimize Recovery

10. BY 2020, 50% OEM REVENUES WILL COME FROM SERVICES, DISRUPTING THE OLD BUSINESS MODEL IN FAVOR OF THE GROWTH-DRIVEN NEW MODEL

  • Smart Solutions to Connect the Oilfield Services Equipment
  • Oilfield Operators-Key Factors for Business Model Transformation
  • Transforming Business Models of Equipment Manufacturers
  • What Should Equipment Manufacturers Do?
  • Business Model Transformation Benefits to OEMs
  • Asset Classes and Analytical Services Offered (Partial Listing)
  • Use Case-Chevron Monitoring Performance by Predictive Maintenance

11. UNFULFILLED NEEDS OF CAPITAL, RESOURCE, AND ASSET EFFICIENCY THREATEN THE SURVIVAL OF AUTOMATION COMPANIES AND DRIVES THEM TOWARD PORTFOLIO EXPANSION

  • Top 5 Objectives of Oil Operators for Automation
  • Oilfield Operator Expectations from Automation Companies
  • Technologies and Domain Expertise-Turning MAC into MIMC*
  • Automation Portfolio Expansion to Drive Smart Business
  • M&A of Automation Companies in the Oilfield
  • Partnerships Driving Portfolio Expansion
  • Use Case 1-Core Automation Moving into Analytics
  • Use Case 1-Core Automation Moving into Analytics (continued)
  • Use Case 2-New Alliances Forming in OFS: Analytics PaaS
  • Use Case 3-Predictive Analytics Reducing Failures
  • Use Case 3-Predictive Analytics Reducing Failures (continued)

12. ICT IN OILFIELD SERVICES TAKES A LEAP OF FAITH WITH AI; THE AGE OF UNMANNED ASSISTANTS HAS ENTERED THE MARKET

  • Evolution of IT Applications-AI Taking Over OFS
  • Arrival of AI & ML in Oilfield Services-Timely & Disruptive
  • Applications of AI and Virtual Assistants Across the Value Chain
  • Business Model Transformation-From Cloud to AI
  • Artificial Intelligence-The New Reality
  • Artificial Intelligence Architecture in Oilfield Services
  • Neural Networks-Stronger in the Oilfield
  • Cognitive Computing Enabling Optimization
  • Machine Learning Application in Oilfield Services
  • Machine Learning Application in Oilfield Services (continued)
  • Partnerships Driving Penetration of Artificial Intelligence
  • Use Case 1-AI Assists in Reservoir Management
  • Use Case 2-Production Optimization for Complex Fields
  • Use Case 3-Convergence Bringing AI to Oilfield Services
  • Use Case 3-Convergence Bringing AI to Oilfield Services (continued)
  • Use Case 4-Repsol-IBM Collaboration on Cognitive Technologies
  • Use Case 5-Finding New Reserve with New Technological Frontiers
  • Artificial Intelligence & Machine Learning Ecosystem

13. TOP RECOMMENDATIONS

  • Top Recommendations
  • Legal Disclaimer

14. APPENDIX

  • Micro Boom Research Methodology
  • List of Abbreviations
  • Additional Sources of Information on Connected Oilfield Services
  • Learn More-Next Steps
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