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Market Research Report

Global AI in Oil and Gas Market - 2021-2028

Published by DataM Intelligence Product code 950563
Published Content info 180 Pages
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Global AI in Oil and Gas Market - 2021-2028
Published: June 17, 2021 Content info: 180 Pages
Description

Market Overview

The global AI in oil and gas market is projected to grow from $XX billion in 2020 to $XX billion in 2021 at a compound annual growth rate (CAGR) of XX%. For the forecast period of 2021-28, the market is expected to grow at a CAGR of XX%.

The implementation of Artificial Intelligence tools like Machine Learning, Deep Neural Networks, Robotics and various others in Oil and Gas Market not only resolves the issues faced by the industry but also helps in making the processes more efficient. The enormous computing power of computer systems in combination with software that exhibits or mimics human characteristics like pattern recognition, plays a major role in the growth of oil and gas market. The market demand for automation of operations and, evaluation and analysis of data is significantly rising. According to Motorola Solutions, the demand for AI in the global oil sector is expected to increase by about 33% by 2035.

Market Dynamics

The driving factors in the global AI oil and gas markets include the increasing safety concerns in the workforce about the aging infrastructure, the analysis of a huge amount of data for improvement in decision making in terms of exploration and production processes. The major restraint is the lack of technical knowledge and training in the workforce. The opportunities include the deployment of innovation in AI for testing as the oil industry revenue allows for research and development.

To optimize the exploration and exploitation of hydrocarbons

Oil and gas producing countries across the world are looking for new approaches to overcome the challenges faced due to present drilling, refining, and data analyzing systems. Hence to make the exploration and exploitation of hydrocarbons more optimized and efficient. Recent applications and researches of AI in these systems have shown that it can be a better approach.

For instance, in 2019 scientists at WIHG (Wadia Institute of Himalayan Geology) came up with a new artificial intelligence technique (based on artificial neural networks) to analyze data from seismic waves (natural or induced by explosive material) to discover the type of rock formation and geological features beneath the surface. This technique could be further applied to exploring the presence of hydrocarbons beneath the surface thus optimizing the process by augmenting AI.

Another recent advancement in Jan 2019, to further advance in exploring the application of cognitive computing and machine learning in its global oil and gas business, Bharat Petroleum (BP) invested in a US-based technology start-up called Belmont Technology to strengthen its AI capabilities by developing a cloud-based geoscience platform.

In Mar 2019, OGA (Oil and Gas Authority) of the UK launched the National Data Repository for all the oil and gas producing ad refining enterprises in the world. It is using AI to analyze data, which, according to the OGA expectations, is likely to assist in discovering new oil and gas forecasts thus simplifying the exploration process.

Artificial Intelligence can help to reduce the effect of brain drain in the industry.

A recent report from the Society of Petroleum Engineers, a global industry organization, found that nearly 54% of its members are over 55 years old which infers that there is a dire need for young talents in the industry. And as the generation of elder workers retires, they aren't being replaced at the same rate by younger employees.

AI can be used to preserve and implement the insights of experienced workers to automate the various operations. For the efficient implementation of data analytics, machine learning is used to recognize patterns in data, and insights from experts will help in devising the required complex algorithms for it.

Intuitive AI-enabled information retrieval systems can be used to capture text and voice inputs from experts and retired. It uses natural language processing which can organize retirees' knowledge and experience in ways that can be transferred to any other worker. The system could be used to create apps to help less-experienced workers identify specialist parts onsite, or even answer questions in the head office using collective, interrogable chat bot-style databases.

One of the primary reasons why AI is used and tested in every industry is its ability to automate previously human-operated and time-consuming functions. It will help lessen the impact of workers taking their skills with them into retirement. After all, if it can automate a job function, the academic knowledge about that particular role is no longer necessary for the organization to function.

Increasing safety concerns among the workforce, especially the maintenance of the aging pipeline infrastructure, is the major driving agent in the growth of AI in the Oil and Gas market. Additionally, the surging incidents of oil & gas leakage from storage tanks and pipelines at production facilities are expected to fuel the market's growth.

AI can help make business decisions. It can identify the need for repairs and infrastructure updates which can help the organizations to allocate investments properly. For example, drones use a combination of on-board (edge) AI and cloud-based AI, along with GPS, to navigate, avoid collision and execute tasks in the field, including surveying and capturing imagery. They can fly over hazardous or uncharted land or even work underwater to send back real-time reports from inspection points, making work safer for humans.

Impact of COVID -19 Analysis

The rapid growth in the year is expected because of the COVID-19 impact on the market. The pandemic led to restrictive containment measures like social distancing, remote working, and the closure of commercial activities that resulted in operational challenges.

The pandemic has caused the world to shift online in learning and education. This can be considered advantageous for the workforce as they can learn skills from the comfort of their home that can help them deploy and operate AI software and tools.

Segment Analysis

The AI in the Oil and Gas market is segmented based on type, function, and application. The AI in the oil and gas market is classified into hardware, software, and services by type segment.

The drilling process of oil and gas production on the surface is quite complex and hazardous because of difficulties like large space, narrow depth, complicated environments and monitoring issues. AI attempts to resolve these issues by using Intelligent well technology which provides services like real-time monitoring, data analytic decision making, remote control of downhole tools. It has several sensors including electronic sensors, fiber optic sensors, and quartz sensors, which are distributed throughout the wellbore to monitor the equipments inside the well and, collect and transmit data like temperature, flow, displacement and time. The data is transmitted to the uphole data analysing system which has softwares implementing reservoir engineering method, optimization method, reservoir numerical simulation and prediction technology to analyse and help operators make immediate decisions.

Another instance, High -Resolution Adaptive Controllers which are used in lifts systems was devised by Calgary-based Ambyint. It integrates with the hardware and instrumentation, such as the motor, controller, variable frequency drive other moving parts of lift systems. The adaptive controllers can deliver real-time control and optimization capabilities at the well, leveraging edge computing capabilities to deliver both physics-based analytics and modern data science in real-time.

Geographical Analysis

The AI in Oil and Gas market is divided into North America, South America, Europe, Asia-Pacific, Middle-East, and Africa. Among all of the regions, North America dominates the AI in Oil and Gas market as it is increasingly adopting AI technologies. The advancement in AI software and system, especially in the United States and Canada is the reason for its dominance. Moreover, factors such as the strong economy and combined investment by government and private organizations for the development and growth of R&D activities are facilitating to incorporate AI in the oil and gas sector, in the region.

For instance, IBM' s Watson computing system which is a cognitive computing-based system is contributing to cut the cost of production in the US, Australia, and Canada's oil sands by increasing efficiency and productivity. The system is proving useful in these challenging times when the oil and gas market is experiencing a slump. In Australia, a dedicated Watson Cognitive Oil Field cognitive system is being piloted by several producers to make exploration and development more productive, efficient, and safer.

For instance, ExxonMobil, one of the leading oil producers in the country, announced its plans to increase the production activity in the Permian Basin of West Texas by producing more than 1 million barrels per day (BPD) of oil-equivalent by as early as 2024. This is equivalent to an increase of nearly 80 percent compared to the present production capacity.

Competitive Analysis

Some of the major organizations that dominate the market are IBM, Amazon, Microsoft, Oracle, Sentient technologies, Inbenta, General Visio, and Cisco (United States). Additionally, the companies that are also part of the research are FuGenX Technologies, Infosys, Hortonworks, and Royal Dutch Shell. Leading multinational players dominate the market and hold substantial market share, thereby presenting tough competition to new entrants. However, there are numerous numbers of companies and start-ups that are continuously researching and testing the various new AI techniques and approaches. An example of a start-up that is progressing rapidly in the field is Belmont Technology These organizations focus on various strategic initiatives such as collaborations, merger & acquisition, geographical expansion, new product launches, and increasing R&D expenditure to stay in the competition. With

For instance, in Sep 2020, Schlumberger, IBM, and Red Hat, announced a collaboration to enhance the AI technology integration in the oil and gas industry. Schlumberger is known for its exploration and production of cloud-based environments and cognitive applications. The collaboration with IBM will provide it with hybrid cloud technology, built on the Red Hat OpenShift container platform.

In October 2019, Microsoft announced the collaboration with energy industry tech company Baker Hughes and AI developer C3.ai to bring enterprise AI technology to the energy industry via its Azure cloud computing platform. It would allow customers to streamline the adoption of AI designed to address inventory, energy management, predictive maintenance, and equipment reliability.

Why Purchase the Report?

Visualize the AI in the Oil and Gas market products in terms of type, function, application, highlighting the critical commercial assets and players.

Identify commercial opportunities in AI in the Oil and Gas market by analyzing trends and co-development deals.

Excel datasheet with thousands of data points of global AI in Oil and Gas market-level4/5segmentation.

PDF report with the most relevant analysis cogently put together after exhaustive qualitative interviews and in-depth market study.

Target Audience

AI manufacturers and suppliers

AI system providers

Environmental research institutes

Government and research organizations

Institutional investors

National and local government organizations

Research organizations and consulting companies

Technology providers

Table of Contents
Product Code: DMIC2527

Table of Contents

1. AI in Oil and Gas Market Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. AI in Oil and Gas Market-Market Definition and Overview

3. AI in Oil and Gas Market-Executive Summary

  • 3.1. Market Snippet by Type
  • 3.2. Market Snippet by Function
  • 3.3. Market Snippet by Application
  • 3.4. Market Snippet by Region

4. AI in Oil and Gas Market-Dynamics

  • 4.1. Market Impacting Factors
    • 4.1.1. Drivers
    • 4.1.2. Restraints
    • 4.1.3. Impact Analysis
    • 4.1.4. Opportunity
    • 4.1.5. Trends

5. AI in Oil and Gas Market-Industry Analysis

  • 5.1. Porter's Five Forces Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Regulatory Analysis
  • 5.4. Pricing Analysis

6. AI in Oil and Gas Market - By Type

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 6.1.2. Market Attractiveness Index, By Type
  • 6.2. Hardware *
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis, USDMn,2019-2028 and Y-o-Y Growth Analysis (%),2021-2028
  • 6.3. Software
  • 6.4. Services

7. AI in Oil and Gas Market - By Function

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 7.1.2. Market Attractiveness Index, By Function
  • 7.2. Predictive Maintenance & Machinery Inspection *
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis, US$ Mn, 2021-2028 and Y-o-Y Growth Analysis (%),2021-2028
  • 7.3. Field Services
  • 7.4. Material Movement
  • 7.5. Quality Control
  • 7.6. Reclamation
  • 7.7. Production Planning

8. AI in Oil and Gas Market - By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Upstream*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis, US$ Mn, 2021-2028 and Y-o-Y Growth Analysis (%), 2018-2026
  • 8.3. Midstream
  • 8.4. Downstream

9. AI in Oil and Gas Market-By Region

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Region
    • 9.1.2. Market Attractiveness Index, By Region
  • 9.2. North America
    • 9.2.1. Introduction
    • 9.2.2. Key Region-Specific Dynamics
    • 9.2.3. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 9.2.4. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 9.2.5. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application
    • 9.2.6. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Country
      • 9.2.6.1. The U.S.
      • 9.2.6.2. Canada
      • 9.2.6.3. Mexico
  • 9.3. Europe
    • 9.3.1. Introduction
    • 9.3.2. Key Region-Specific Dynamics
    • 9.3.3. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 9.3.4. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 9.3.5. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application
    • 9.3.6. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Country
      • 9.3.6.1. Germany
      • 9.3.6.2. The U.K.
      • 9.3.6.3. France
      • 9.3.6.4. Rest of Europe
  • 9.4. South America
    • 9.4.1. Introduction
    • 9.4.2. Key Region-Specific Dynamics
    • 9.4.3. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 9.4.4. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 9.4.5. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application
    • 9.4.6. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Country
      • 9.4.6.1. Brazil
      • 9.4.6.2. Argentina
      • 9.4.6.3. Rest of South America
  • 9.5. Asia Pacific
    • 9.5.1. Introduction
    • 9.5.2. Key Region-Specific Dynamics
    • 9.5.3. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 9.5.4. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 9.5.5. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application
    • 9.5.6. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Country
      • 9.5.6.1. China
      • 9.5.6.2. India
      • 9.5.6.3. Japan
      • 9.5.6.4. Australia
      • 9.5.6.5. Rest of Asia Pacific
  • 9.6. The Middle East and Africa
    • 9.6.1. Introduction
    • 9.6.2. Key Region-Specific Dynamics
    • 9.6.3. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Type
    • 9.6.4. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Function
    • 9.6.5. Market Size Analysis, and Y-o-Y Growth Analysis (%), By Application

10. AI in Oil and Gas Market Competitive Landscape

  • 10.1. Competitive Scenario
  • 10.2. Market Positioning/Share Analysis
  • 10.3. Mergers and Acquisitions Analysis

11. AI in Oil and Gas Market Company Profiles

  • 11.1. IBM
    • 11.1.1. Company Overview
    • 11.1.2. Form Portfolio and Description
    • 11.1.3. Key Highlights
    • 11.1.4. Financial Overview
  • 11.2. Intel
  • 11.3. Google
  • 11.4. Microsoft
  • 11.5. Oracle
  • 11.6. Sentient technologies
  • 11.7. Inbenta
  • 11.8. General Vision
  • 11.9. Cisco
  • 11.10. Hortonworks (*List Is Not Exhaustive)

12. AI in Oil and Gas Market-Premium Insights

13. AI in Oil and Gas Market-DataM

  • 13.1. Appendix
  • 13.2. About Us and Services
  • 13.3. Contact Us
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