Comprehensive Report on Artificial Intelligence in Oil & Gas Market CAGR of +12% by 2022: Trends, Analysis by Regions, Type, Application, Market Drivers, Restraints

After several years of research on machine learning algorithms running on oil and gas production data, Solution Seeker has developed a hierarchical neural network model that improves the predictive power for real-time production optimization. The model leverages the power of neural network learning algorithms combined with domain knowledge in the form of first principle physics and production system logic.


Pune, India -- (SBWIRE) -- 03/07/2018 -- The AI in Oil & Gas Market to Grow steadily at a CAGR of +12% during the forecast period.

The growth of AI in Oil & Gas market can be attributed to the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Concerns regarding data security and cybersecurity are the major challenges faced by the players in this market.

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.
The report segments AI in the Oil & Gas market, on the basis of type, into hardware, software, and services.

The software segment led AI in the Oil & Gas market in 2016. Software in AI in the Oil & Gas market are applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in the Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

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 2018

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