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Machine Learning in Oil & Gas - Thematic Research

  • ID: 4662112
  • Report
  • September 2018
  • Region: Global
  • 39 pages
  • GlobalData
1h Free Analyst Time

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Machine Learning in Oil & Gas - Thematic Research

Summary

Machine learning is an Artificial Intelligence (AI) technology which allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.

Oil and Gas industry is going through a challenging phase after having to face disruptions in the form of sluggish oil prices and opposition to hydrocarbon exploration and production from environmentalists. In such a scenario, Oil and Gas companies are increasingly turning towards digital technologies, such as machine learning, to improve operational efficiency, cost savings, and reducing environmental impact of exploration and production activities through predictive maintenance.

The most prominent application for machine learning presently, is predictive maintenance. Sensor data related to drilling wells, pipeline infrastructure and other critical equipment is monitored regularly and assessed to predict equipment failures and plan maintenance activities proactively.

Real-time data generated from various industrial activities is being captured and analyzed using machine learning algorithms to enable field operators in taking informed decisions for achieving higher productivity and cost savings, while also ensuring the overall health and safety of the assets.

Scope
  • The report provides impact of machine learning on the oil & gas industry.
  • The report discusses how oil and gas industry is benefiting from machine learning techniques in predictive maintenance of equipment and scheduling maintenance programs to proactively fix the faults.
  • The report analyses how oil and gas companies are transforming themselves by deploying machine learning and other digital technologies
  • The report further shows the adoption of machine learning techniques in analysis of seismic data for detecting and quantifying hydrocarbon reserves.
  • The report also shows how machine learning helps in efficiency improvement across various activities in the oil and gas industry.
Reasons to buy
  • To understand emerging trends and applications for machine learning in the oil and gas industry.
  • Know case studies demonstrating how oil and gas companies are using machine learning for enhanced operational performance and improved business competitiveness.
  • To Know more about companies in the oil and gas sector, which have strong competitive position in the machine learning theme.
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PLAYERS

TECHNOLOGY BRIEFING
  • Definitions
  • Ten key AI technologies
  • History of machine learning
  • How does deep learning work?
TRENDS
  • Technology trends
  • Macro-economic trends
  • Use case trends
  • Applications of Machine Learning in Oil & Gas
VALUE CHAIN
  • Hardware enablers
  • Optimised networking equipment
  • High end processors
  • Communication chips
  • Embedded chips
  • Software enablers
  • Master data management
  • AI engine
  • Developer tools (APIs and SDKs)
  • Software with embedded AI
INDUSTRY ANALYSIS
  • AI and ML likely to become widespread because too much is open sourced
  • AI and ML are transforming the semiconductors market
  • Timeline
IMPACT OF MACHINE LEARNING ON OIL & GAS
  • Oil & gas case studies in ML
COMPANIES SECTION
  • Listed tech companies
  • Privately held tech companies
  • Oil & gas companies
APPENDIX: “THEMATIC” RESEARCH METHODOLOGY
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