AI in Oil and Gas Market Size - Growth, Trends, and Forecast (2019 - 2024)

The AI in Oil and Gas Market is Segmented by Application (Quality Control, Production Planning, Predictive Maintenance, Thermal Detection), Operation (Upstream, Midstream, Downstream), Service Type, and Geography.

Market Snapshot

Study Period:


Base Year:


Fastest Growing Market:

North America

Largest Market:

North America

Major Players:

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Market Overview

The AI in Oil and Gas market was valued at USD 2 billion in 2019 and is expected to reach USD 3.98 million by 2025, at a CAGR of 12.14% over the forecast period 2020 - 2025. As the cost of IoT sensors declines, more major oil and gas organizations are bound to start integrating these sensors into their upstream, midstream, and downstream operations along with AI-enabled predictive analytics.

  • The increasing demand for big data technology in the oil and gas industry to augment E&P capabilities with the growing need for automation in the oil and gas industry is thereby increasing the investments through joint venture capitals. Since artificial intelligence systems can optimize and automate data-rich processes, they help in minimizing or eliminating duplication of efforts and further in mitigating business risk. This enhances productivity and minimizes the overall operational cost.
  • The increasing demand for big data technology in the oil and gas industry to augment E&P capabilities with the growing need for automation in the oil and gas industry is, thereby, increasing the investments through joint venture capitals.
  • However, high capital investments for the integration of AI technologies, along with the lack of skilled AI professionals, could hinder the growth of the market.

Scope of the Report

The upstream sector of the oil and gas industry includes searching for potential underground or underwater crude oil and natural gas fields, drilling exploratory wells, and subsequently drilling and operating the wells that recover and bring the crude oil or raw natural gas to the surface. The midstream sector involves transportation (by pipeline, rail, barge, oil tanker, or truck), storage, and wholesale marketing of crude or refined petroleum products. Pipelines and other transport systems can be used to move crude oil from production sites to refineries and deliver the various refined products to downstream distributors. The downstream sector is the refining of petroleum crude oil and the processing and purifying of raw natural gas as well as the marketing and distribution of products derived from crude oil and natural gas.

By Application
Quality Control
Production Planning
Predictive Maintenance
Thermal Detection (Drone Analytics)
Other Applications
By Operation
By Service Type
North America
United States
United Kingdom
Rest of Europe
Rest of Asia-Pacific
Latin America
Rest of Latin America
Middle East & Africa
United Arab Emirates
Saudi Arabia
Rest of Middle-East & Africa

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Key Market Trends

Predictive Maintenance is Expected to Hold a Major Market Share during the Forecast Period

  • With the increasing demand for oil and gas, the manufacturers have started focussing on the discovery of the new oil wells. The predictive maintenance helps the manufacturer to save the money as the input cost which is majorly dominated by the maintenance costs are reduced due to the continuous monitoring being held.
  • Oil and gas companies store crude and refined oils in large tanks and transport it through pipelines. As crude oil from oil fields usually varies in its chemical compositions,  the corrosiveness of the crude also depends on the environment it is stored in. Corrosion caused by crude oils is one of the prevalent risks for equipment failures in the oil and gas industries.
  • Corrosion engineers are employed for this purpose in order to keep the machinery corrosion-free. AI has come into the picture recently with its software-driven solutions. For instance, Maana, Palo Alto California-based company, offers software called Computational Knowledge Graph, which it claims can help oil and gas companies reduce unplanned maintenance from oil corrosion using predictive analytics.
  • The data collected over several years can be used to improve the efficiency of their predictive maintenance techniques. With AI getting implemented at a broader level in the oil and gas industry, the manufacturers can now take advantage of it to improvise on their maintenance techniques to reduce machine failures. For instance, Alejandro Betancourt, the lead of the analytics team at Columbian oil and gas company Ecopetrol, seemed to suggest that the oil and gas industry has collected data over the years that might have been curated and analyzed by human experts, making it ripe for feeding into AI systems. A large part of this data includes exploration, production, and reservoir data logs. This helps in analyzing the failure patterns of the machinery this powering the predictive maintenance techniques positively.
  • With global emission policies tightening up, the limelight of the industry has shifted to the predictive maintenance of the machinery. With proper maintenance strategies, a company can control carbon emissions, thus, reducing the threat of government penalties.

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North America is Expected to Hold a Major Market Share during the Forecast Period

Owing to the increasing adoption of AI technologies across the oilfield operators and service providers and the robust presence of prominent AI software and system suppliers, especially in the United States and Canada, the North American segment is anticipated to account for the largest share of the AI in the oil and gas market, over the forecast period.

Owing to the increasing inflow of investments in startups for AI implementation, which would further augment the demand for AI in the near future, the region is poised to be the fastest-growing segment. Some of the prominent players of the North American region are Google LLC, IBM Corp., FuGenX Technologies Pvt. Ltd, Hortonworks Inc., Microsoft Corporation, and Intel Corp., among others.

The largest oil and gas companies in the United States are poised to impact the market positively, as they embrace the latest technologies and innovations. They launched an American energy renaissance, leading to new natural gas finds and expansion of oil production from reserves that were once deemed unavailable.

‘The Environmental Partnership’ is an industry-led initiative created to help the largest oil and gas companies in the United States to work together and continuously negate adverse environmental impacts. It’s a landmark collaboration that’s initially focused on further reducing emissions from oil and gas production in the United States. This shows the shift toward growing environmental concerns. Artificial Intelligence can facilitate the control of carbon emissions by streamlining the predictive maintenance techniques.

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Competitive Landscape

The AI in the oil and gas market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. The companies are continuously capitalizing on acquisitions, in order to broaden, complement, and enhance its product and service offerings, to add new customers and certified personnel, and to help expand sales channels. 

October 2018 - IBM recently acquired Red Hat to position itself as a cloud power. With the deal, IBM carves out a place for itself that's separate from the top cloud providers. Whereas Amazon, Microsoft, and Google are primarily public cloud and software providers, IBM specializes in hybrid cloud, offering a deep hardware and software stack stretching back through literally 60 years of enterprise legacy, and looking ahead to the containerized and AI-enabled future.

Table Of Contents


    1. 1.1 Study Assumptions

    2. 1.2 Scope of the Study




    1. 4.1 Market Overview

    2. 4.2 Market Drivers

      1. 4.2.1 Growing Investment on Big Data across the Energy and Power Sector

      2. 4.2.2 Increasing Need to Handle the Significant Volume of Data Generated from IoT

      3. 4.2.3 Rising Trend of Automation to Reduce Production Cost and Streamline Wastage

    3. 4.3 Market Restraints

      1. 4.3.1 High Initial Investment Costs in AI Implementation

      2. 4.3.2 Lack of Skilled Professional across the Oil and Gas Industry

    4. 4.4 Industry Attractiveness - Porter's Five Forces Analysis

      1. 4.4.1 Threat of New Entrants

      2. 4.4.2 Bargaining Power of Buyers/Consumers

      3. 4.4.3 Bargaining Power of Suppliers

      4. 4.4.4 Threat of Substitute Products

      5. 4.4.5 Intensity of Competitive Rivalry


    1. 5.1 By Application

      1. 5.1.1 Quality Control

      2. 5.1.2 Production Planning

      3. 5.1.3 Predictive Maintenance

      4. 5.1.4 Thermal Detection (Drone Analytics)

      5. 5.1.5 Other Applications

    2. 5.2 By Operation

      1. 5.2.1 Upstream

      2. 5.2.2 Midstream

      3. 5.2.3 Downstream

    3. 5.3 By Service Type

      1. 5.3.1 Professional

      2. 5.3.2 Managed

    4. 5.4 Geography

      1. 5.4.1 North America

        1. United States

        2. Canada

      2. 5.4.2 Europe

        1. United Kingdom

        2. Norway

        3. Russia

        4. Rest of Europe

      3. 5.4.3 Asia-Pacific

        1. China

        2. India

        3. Australia

        4. Rest of Asia-Pacific

      4. 5.4.4 Latin America

        1. Mexico

        2. Brazil

        3. Argentina

        4. Rest of Latin America

      5. 5.4.5 Middle East & Africa

        1. United Arab Emirates

        2. Saudi Arabia

        3. Qatar

        4. Rest of Middle-East & Africa


    1. 6.1 Company Profiles

      1. 6.1.1 Google LLC

      2. 6.1.2 IBM Corporation

      3. 6.1.3 FuGenX Technologies Pvt. Ltd.

      4. 6.1.4 Hortonworks Inc.

      5. 6.1.5 Microsoft Corporation

      6. 6.1.6 Intel Corporation

      7. 6.1.7 Royal Dutch Shell PLC

      8. 6.1.8 PJSC Gazprom Neft

      9. 6.1.9 Huawei Technologies Co., Ltd.

      10. 6.1.10 NVIDIA Corp.

      11. 6.1.11 Infosys Ltd.

    2. *List Not Exhaustive


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