Machine Learning in Oil and Gas – Thematic Intelligence
Machine Learning in Oil and gas Thematic Overview
Machine learning will benefit companies in the oil and gas sector by driving automation, process improvement, and demand forecasting. It will support in modernizing maintenance practices, detecting leakages, streamlining data management and documentation, optimizing inventory and supply chains.
The oil and gas industry has experienced two massive disruptions in just three years in the form of COVID-19 and the Ukraine conflict. While the former impacted global energy demand, the latter caused upheaval in oil and gas supply chains following the sanctions on the world’s top energy supplier Russia. This has necessitated increased oversight and performance optimization across all functions project design, construction, logistics, inventory management, and maintenance. Above all, companies also want better oversight into market demand to align their production The goal is to find every opportunity to lower costs to sustain in the long term.
Machine learning in oil & gas market research report presents an overview of adoption of machine learning technologies in the oil and gas industry. It analyses the value chain, the challenges faced by the oil and gas industry, and how machine learning is enabling the industry to tackle these challenges.
|Value Chains||Software, Hardware, Services, and Use Cases|
|Leading Public Companies||Alibaba, Alphabet (parent company of Google), Amazon, Arm, and Baidu|
|Leading Private Companies||Cerebras, Cloudera, Databricks, Groq, and Horizon Robotics.|
|Leading Oil and Gas Companies Associated with ML Theme||BP, ExxonMobil, Gazprom, and Petronas|
Machine Learning in Oil and Gas – Trends
The main trends shaping the machine learning theme over the next 12 to 24 months are classified as technology trends, macroeconomic trends, industry trends, and regulatory trends.
Technology trends: The key technology trends impacting the machine learning theme are innovation in technologies such as Explainable AI (XAI), facial recognition (FR), federated learning, natural language processing (NLP), AI chips.
Macroeconomic trends: The key macroeconomic trends impacting the machine learning theme are focus on ESG – environment, rising influence of China and its focus on improving technology, and COVID-19.
Industry trends: The key industry trends impacting the machine learning theme are garnering insights from digitalization, extracting superior performance, and management of asset lifecycle.
Regulatory trends: The key regulatory trends impacting the machine learning theme are data privacy, algorithmic bias regulation, and lack of global AI regulation.
Machine Learning in Oil and Gas – Industry Analysis
Machine learning plays a role in virtually every industry, putting pressure on incumbents to adapt and innovate or face stagnation and possible elimination. GlobalData’s AI forecasts focus on four technology segments, including specialized AI applications, AI consulting and support services, AI platforms, and AI hardware.
Specialized AI applications: This category includes horizontal applications embedded with AI-driven features such as image recognition, natural language processing (NLP), and sentiment analysis.
The Machine learning in oil and gas industry analysis also covers:
- Market size and growth forecasts
- Mergers and acquisitions
- Venture financing
- Patent trends
- Hiring trends
- Machine learning timeline
Machine in Oil and Gas Market Size 2019-2026 ($ Billion)
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Machine Learning in Oil and Gas- Value Chain Analysis
The machine learning in oil and gas value chain consists of software, hardware, services, and use cases.
Hardware: The hardware dictates the speed and capacity in which data can be transferred and, therefore, the speed and latency of the machine learning system. Hardware, including sensors, storage, servers, and networking equipment, collects, processes, and transfers data for machine learning inputs.
Machine Learning in Oil and Gas Value Chain Analysis
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Segments Covered in this Report
Leading Public Companies Associated with Machine Learning Theme
- Alphabet (parent company of Google)
Leading Private Companies Associated with Machine Learning Theme
- Horizon Robotics
Leading Oil and Gas Companies Associated with Machine Learning Theme
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Integrated Oil and Gas Sector Scorecard
At GlobalData, we use a scorecard approach to predict tomorrow’s leading companies within each sector. Our sector scorecard has three screens: a thematic screen, a valuation screen, and a risk screen.
- The thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance.
- The valuation screen ranks our universe of companies within a sector based on selected valuation metrics.
- The risk screen ranks companies within a particular sector based on overall investment risk.
Integrated Oil and Gas Sector Scorecard – Thematic Screen
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- This report presents an overview of growth of machine learning technologies with special focus on adoption of machine learning in oil and gas industry.
- It analyses the machine learning value chain in terms of hardware, software, and services, and identifies key players across the value chain.
- It evaluates the market growth trends, M&A activity, venture financing, patent, and hiring trends in the machine learning theme.
- The report provides an overview of the competitive positions held by public as well as private machine learning technology vendors as well as adoption among oil and gas companies.
- It also highlights machine learning use cases by the oil and gas players
Reasons to Buy
- Evaluates the machine value chain and highlights major players in each segment.
- Impact analysis of machine learning on the oil and gas industry.
- Review of some of the use cases highlighting the adoption of machine learning by the oil and gas players.
- Identify and benchmark key oil and gas companies and their involvement in the machine learning theme.
- Identify and benchmark key public and private technology vendors shaping the machine learning market.
Table of Contents
- 1. Executive Summary
- 2. Players
- 3. Technology Briefing
- 4. Trends
- 4.1. Technology trends
- 4.2. Macroeconomic trends
- 4.3. Regulatory trends
- 4.4. Industry trends
- 5. Impact on the Oil and Gas Industry
- 5.1. Case studies
- 6. Industry Analysis
- 6.1. Market size and growth forecasts
- 6.2. Mergers and acquisitions
- 6.3. Venture financing
- 6.4. Patent trends
- 6.5. Hiring trends
- 6.6. Timeline
- 7. Value Chain
- 7.1. Hardware
- 7.2. Software
- 7.3. Services
- 8. Companies
- 8.1. Public companies
- 8.2. Private companies
- 8.3. Oil and gas companies
- 9. Sector Scorecard
- 9.1. Integrated oil and gas sector scorecard
- 9.2. Independent oil and gas sector scorecard
- 10. Glossary
- 11. Further Reading
- 12. Our Thematic Research Methodology
- 13. About GlobalData
- 14. Contact Us
Frequently Asked Questions
The Machine learning in oil and gas value chain consists of software, hardware, services, and use cases.
Some of the leading public companies associated with machine learning theme are Alibaba, Alphabet (parent company of Google), Amazon, Arm, and Baidu.
Some of the leading private companies associated with machine learning theme are Cerebras, Cloudera, Databricks, Groq, and Horizon Robotics.
Leading oil and gas companies associated with machine learning theme are BP, ExxonMobil, Gazprom, and Petronas.
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