Complex algorithms enable you to make decisions automatically through predictive analysis and machine learning, and data analysis provides a systematic way to capture a large amount of data collected from the entire oil and gas value chain. This allows you to predict when an asset will fail or ensure that resources and personnel are deployed where needed. With unparalleled optimization and improved safety forecasting analysis and machine learning, asset monitoring and analysis has become more important than ever before in the industry.
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The AI in Oil & Gas Market to Grow steadily at a CAGR of +12% during the forecast period.
The report on the Global AI in Oil & Gas Market is a complete overview of the market, covering various aspects product definition, segmentation based on various parameters, and the prevailing vendor landscape. It compiles in-depth information and research methodologies.
It is also combined with relevant charts and tables to enable readers to get a better perspective of this global market.
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Companies Profiled in this report includes IBM, Intel, Microsoft, Accenture, Google, Microsoft, Oracle, Numenta, Sentient technologies, Inbenta, General Vision, Cisco, Infosys, Hortonworks, and Royal Dutch Shell.
Predictive maintenance and machine testing is expected to be the largest in the oil and gas market in terms of function by function in 2017. Predictive maintenance solutions are used for operational data analysis, such as drill maintenance or downtime and real-time decision making.
Develop a model that can predict the equipment failure mode. Predictive maintenance is applicable to all streams in the oil and gas sector and is critical to ensuring uninterrupted operation by predicting accurate equipment conditions to avoid costly downturns.
The research lists key companies operating in the global market and also highlights the key changing trends adopted by the companies to maintain their dominance. By using SWOT analysis and Porter’s five force analysis tools, the strengths, weaknesses, opportunities, and threats of key companies are mentioned in the report.
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As the global AI in Oil & Gas market is segmented based on various parameters, an in-depth classification of the market is also mentioned; elements impacting the market’s growth are studied in detail to understand the report precisely. Moreover this, profiles of some of the leading players operating in the global AI in Oil & Gas market are included in the report.
Using SWOT analysis, their weaknesses and strengths are analyzed. It helps the study deliver visions into the opportunities and threats that companies may face during the forecast period.
Furthermore, the report profiles some of the most prominent enterprises in the global AI in Oil & Gas market to provide valuable recommendations. The product portfolio of the companies profiled are studied in detail.
Besides this, information is obtained from their financial reports and strategies they adopted over the last couple of years.
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Table of Contents
Global AI in Oil & Gas Market Research Report 2017
Chapter 1 Global AI in Oil & Gas Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Market Forecast
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