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石油・ガスにおけるAI市場レポート：タイプ、機能、用途、地域別、2024年～2032年

AI in Oil and Gas Market Report by Type, Function, Application, and Region 2024-2032
出版日: | 発行: IMARC | ページ情報: 英文 137 Pages | 納期: 2～3営業日

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石油・ガスにおけるAI市場レポート：タイプ、機能、用途、地域別、2024年～2032年
出版日: 2024年04月08日
発行: IMARC
ページ情報: 英文 137 Pages
納期: 2～3営業日
  • 全表示
  • 概要
  • 図表
  • 目次
概要

世界の石油・ガスにおけるAI市場規模は2023年に27億米ドルに達しました。今後、IMARC Groupは、2024年から2032年にかけて8.61%の成長率（CAGR）を示し、2032年までに57億米ドルに達すると予測しています。石油・ガス業界におけるデータの爆発的増加、業務効率化の要求の高まり、安全性の重視の高まり、厳しい環境規制の賦課、人工知能（AI）アルゴリズムにおける最近の技術進歩は、市場を推進している主な要因の一部です。

石油・ガスにおけるAIとは、石油・ガス産業におけるプロセスの最適化、安全性の向上、意思決定の改善における人工知能（AI）技術の応用を指します。これには、ニューラルネットワーク、コンピュータビジョン、機械学習（ML）、ロボット工学、自然言語処理（NLP）が含まれます。石油・ガス産業におけるAIは、貯留層シミュレーション、自動掘削、予知保全、地質マッピング、安全モニタリング、プロセス自動化、資産管理などに広く利用されています。意思決定、コスト削減、安全性の向上、信頼性の改善、拡張性の強化、持続可能性の促進を支援します。

石油・ガス業界全体でリソースを最適化し、業務を合理化できる費用対効果の高いソリューションを提供するためにAIが広く採用されていることが、市場の成長を促進しています。さらに、より良いコンプライアンスと石油・ガス採掘時の二酸化炭素排出量を最小化するためにAIを使用することを企業に強制している厳しい環境規制の賦課は、市場成長にプラスの影響を与えています。さらに、AIアルゴリズムの最近の技術的進歩は、AIベースのソリューションの実装をより実用的かつ効率的にする計算能力の向上と相まって、市場の成長を支えています。このほか、複雑な作業をこなす熟練者の不足が高まっていることが、石油・ガスの掘削作業の自動化を可能にするAIの需要を促進しています。その他、持続可能性の重視の高まり、業務の透明性に対する需要の高まり、石油・ガス探査の増加などが、市場成長の原動力になると予想されます。

石油・ガスにおけるAI市場動向と促進要因：

石油・ガス業界におけるデータ爆発の増加

石油・ガス分野では、センサー、掘削装置、その他さまざまな運用技術から、これまでにない量のデータが生成されています。従来のデータ分析ツールと比較して、AIはこのデータをリアルタイムで効果的に管理・解釈するために広く利用されています。さらに、膨大なデータセットを閲覧し、パターン、動向、異常を調べることができる高度な分析機能を提供します。これとは別に、AIは生データを掘削作業の監視やサプライチェーンプロセスの最適化に活用できる有用な洞察に変換するツールを提供します。さらに、企業はデータの意味を理解し、よりスマートな意思決定のための戦略的資産に変えるために、AIソリューションへの投資を増やしています。

高まる業務効率化の要求

石油・ガス分野では、複雑で危険な作業が多く、綿密な計画と実行が求められます。さらに、人為的なミスや機器の故障、操業のどの部分においても非効率が生じると、多大な金銭的損失や安全上のリスクが生じる可能性があります。これに伴い、AI技術、特に機械学習（ML）と予測分析は、これらのオペレーションを大幅に最適化する能力を提供します。さらに、機器の故障を事前に予測し、反復作業を自動化し、掘削・抽出プロセスの精度を向上させることができます。さらに、AIはコストを削減するだけでなく、手作業によるミスやシステム障害に伴うリスクも最小限に抑えることができます。その結果、操業効率は石油・ガス産業におけるAI統合の主な促進要因となっています。

安全性重視の高まり

石油・ガス産業では、深海掘削や可燃性の高い物質を扱うなど、危険な作業が多いため、安全性が重視される傾向が強まっており、市場の成長を後押ししています。さらに、従来の安全対策では、事故や故障を完全に排除できないことが多いです。これに伴い、AIはリアルタイムのモニタリング、予測分析、自動制御システムを通じて安全プロトコルの高度なレイヤーを提供します。AIは複数のセンサーからのデータを分析し、潜在的な事故の兆候となる異常を検出することができるため、事故が発生する前に予防措置を講じることができます。さらに、AIは危険性の高い特定の作業を自動化し、潜在的に危険なシナリオにおける手動介入の必要性を減らすことができます。その結果、安全対策を強化するためのAI技術の採用は、市場の成長を後押しする重要な要因となっています。

目次

第1章 序文

第2章 調査範囲と調査手法

  • 調査目的
  • 利害関係者
  • データソース
    • 一次情報
    • 二次情報
  • 市場推定
    • ボトムアップアプローチ
    • トップダウンアプローチ
  • 調査手法

第3章 エグゼクティブサマリー

第4章 イントロダクション

  • 概要
  • 主要産業動向

第5章 石油・ガスにおけるAIの世界市場

  • 市場概要
  • 市場実績
  • COVID-19の影響
  • 市場予測

第6章 市場内訳：タイプ別

  • ハードウェア
    • 市場動向
    • 市場予測
  • ソフトウェア
    • 市場動向
    • 市場予測
  • サービス
    • 市場動向
    • 市場予測

第7章 市場内訳：機能別

  • 予知保全・機械点検
    • 市場動向
    • 市場予測
  • 材料の動き
    • 市場動向
    • 市場予測
  • 生産計画
    • 市場動向
    • 市場予測
  • フィールドサービス
    • 市場動向
    • 市場予測
  • 品質管理
    • 市場動向
    • 市場予測
  • 再生利用
    • 市場動向
    • 市場予測

第8章 市場内訳：用途別

  • 上流
    • 市場動向
    • 市場予測
  • 川下
    • 市場動向
    • 市場予測
  • 中流
    • 市場動向
    • 市場予測

第9章 市場内訳：地域別

  • 北米
    • 米国
    • カナダ
  • アジア太平洋
    • 中国
    • 日本
    • インド
    • 韓国
    • オーストラリア
    • インドネシア
    • その他
  • 欧州
    • ドイツ
    • フランス
    • 英国
    • イタリア
    • スペイン
    • ロシア
    • その他
  • ラテンアメリカ
    • ブラジル
    • メキシコ
    • その他
  • 中東・アフリカ
    • 市場動向
    • 市場内訳：国別
    • 市場予測

第10章 SWOT分析

  • 概要
  • 強み
  • 弱み
  • 機会
  • 脅威

第11章 バリューチェーン分析

第12章 ポーターのファイブフォース分析

  • 概要
  • 買い手の交渉力
  • 供給企業の交渉力
  • 競合の程度
  • 新規参入業者の脅威
  • 代替品の脅威

第13章 価格分析

第14章 競合情勢

  • 市場構造
  • 主要企業
  • 主要企業のプロファイル
    • Accenture plc
    • C3.AI Inc.
    • Cisco Systems Inc.
    • Cloudera Inc.
    • Fugenx Technologies
    • Huawei Technologies Co. Ltd
    • Infosys Limited
    • Intel Corporation
    • International Business Machines Corporation
    • Microsoft Corporation
    • Neudax
    • Nvidia Corporation
    • Oracle Corporation
    • Shell plc
図表

List of Figures

  • Figure 1: Global: AI in Oil and Gas Market: Major Drivers and Challenges
  • Figure 2: Global: AI in Oil and Gas Market: Sales Value (in Billion US$), 2018-2023
  • Figure 3: Global: AI in Oil and Gas Market Forecast: Sales Value (in Billion US$), 2024-2032
  • Figure 4: Global: AI in Oil and Gas Market: Breakup by Type (in %), 2023
  • Figure 5: Global: AI in Oil and Gas Market: Breakup by Function (in %), 2023
  • Figure 6: Global: AI in Oil and Gas Market: Breakup by Application (in %), 2023
  • Figure 7: Global: AI in Oil and Gas Market: Breakup by Region (in %), 2023
  • Figure 8: Global: AI in Oil and Gas (Hardware) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 9: Global: AI in Oil and Gas (Hardware) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 10: Global: AI in Oil and Gas (Software) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 11: Global: AI in Oil and Gas (Software) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 12: Global: AI in Oil and Gas (Services) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 13: Global: AI in Oil and Gas (Services) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 14: Global: AI in Oil and Gas (Predictive Maintenance and Machinery Inspection) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 15: Global: AI in Oil and Gas (Predictive Maintenance and Machinery Inspection) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 16: Global: AI in Oil and Gas (Material Movement) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 17: Global: AI in Oil and Gas (Material Movement) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 18: Global: AI in Oil and Gas (Production Planning) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 19: Global: AI in Oil and Gas (Production Planning) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 20: Global: AI in Oil and Gas (Field Services) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 21: Global: AI in Oil and Gas (Field Services) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 22: Global: AI in Oil and Gas (Quality Control) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 23: Global: AI in Oil and Gas (Quality Control) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 24: Global: AI in Oil and Gas (Reclamation) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 25: Global: AI in Oil and Gas (Reclamation) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 26: Global: AI in Oil and Gas (Upstream) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 27: Global: AI in Oil and Gas (Upstream) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 28: Global: AI in Oil and Gas (Downstream) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 29: Global: AI in Oil and Gas (Downstream) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 30: Global: AI in Oil and Gas (Midstream) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 31: Global: AI in Oil and Gas (Midstream) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 32: North America: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 33: North America: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 34: United States: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 35: United States: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 36: Canada: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 37: Canada: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 38: Asia-Pacific: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 39: Asia-Pacific: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 40: China: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 41: China: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 42: Japan: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 43: Japan: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 44: India: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 45: India: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 46: South Korea: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 47: South Korea: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 48: Australia: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 49: Australia: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 50: Indonesia: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 51: Indonesia: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 52: Others: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 53: Others: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 54: Europe: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 55: Europe: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 56: Germany: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 57: Germany: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 58: France: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 59: France: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 60: United Kingdom: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 61: United Kingdom: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 62: Italy: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 63: Italy: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 64: Spain: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 65: Spain: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 66: Russia: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 67: Russia: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 68: Others: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 69: Others: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 70: Latin America: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 71: Latin America: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 72: Brazil: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 73: Brazil: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 74: Mexico: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 75: Mexico: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 76: Others: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 77: Others: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 78: Middle East and Africa: AI in Oil and Gas Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 79: Middle East and Africa: AI in Oil and Gas Market: Breakup by Country (in %), 2023
  • Figure 80: Middle East and Africa: AI in Oil and Gas Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 81: Global: AI in Oil and Gas Industry: SWOT Analysis
  • Figure 82: Global: AI in Oil and Gas Industry: Value Chain Analysis
  • Figure 83: Global: AI in Oil and Gas Industry: Porter's Five Forces Analysis

List of Tables

  • Table 1: Global: AI in Oil and Gas Market: Key Industry Highlights, 2023 and 2032
  • Table 2: Global: AI in Oil and Gas Market Forecast: Breakup by Type (in Million US$), 2024-2032
  • Table 3: Global: AI in Oil and Gas Market Forecast: Breakup by Function (in Million US$), 2024-2032
  • Table 4: Global: AI in Oil and Gas Market Forecast: Breakup by Application (in Million US$), 2024-2032
  • Table 5: Global: AI in Oil and Gas Market Forecast: Breakup by Region (in Million US$), 2024-2032
  • Table 6: Global: AI in Oil and Gas Market: Competitive Structure
  • Table 7: Global: AI in Oil and Gas Market: Key Players
目次
Product Code: SR112024A6014

The global AI in oil and gas market size reached US$ 2.7 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 5.7 Billion by 2032, exhibiting a growth rate (CAGR) of 8.61% during 2024-2032. The increasing data explosion in the oil and gas industry, rising demand for operational efficiency, growing emphasis on safety, imposition of strict environmental regulations, and the recent technological advancements in artificial intelligence (AI) algorithms are some of the major factors propelling the market.

AI in oil and gas refers to the application of artificial intelligence (AI) technologies in optimizing processes, enhancing safety, and improving decision-making in the oil and gas industry. It includes neural networks, computer visions, machine learning (ML), robotics, and natural language processing (NLP). AI in oil and gas is widely used in reservoir simulation, automated drilling, predictive maintenance, geological mapping, safety monitoring, process automation, and asset management. It aids in decision-making, reducing costs, increasing safety, improving reliability, enhancing scalability, and promoting sustainability.

The widespread adoption of AI to provide cost-effective solutions that can optimize resources and streamline operations across the oil and gas industry is propelling the market growth. Furthermore, the imposition of strict environmental regulations that are compelling firms to use AI for better compliance and to minimize their carbon footprint during oil and gas extraction is positively influencing the market growth. Additionally, the recent technological advancements in AI algorithms, coupled with increased computational power, which makes it more practical and efficient to implement AI-based solutions, are supporting the market growth. Besides this, the rising shortage of skilled personnel for complex tasks is facilitating the demand for AI to enable automation capabilities across various oil and gas drilling operations. Other factors, including the growing emphasis on sustainability, increasing demand for operational transparency, and rising oil and gas exploration, are anticipated to drive the market growth.

AI in Oil and Gas Market Trends/Drivers:

The increasing data explosion in the oil and gas industry

The oil and gas sector is generating an unprecedented volume of data stemming from sensors, drilling equipment, and various other operational technologies. As compared to traditional data analytics tools, AI is widely used to effectively manage and interpret this data in real-time, which is something. Furthermore, it offers advanced analytics capabilities that can browse through vast data sets to examine patterns, trends, and anomalies. Apart from this, AI provides the tools to transform raw data into useful insights that can be utilized for monitoring drilling operations and optimizing the supply chain processes. Moreover, companies are increasingly investing in AI solutions to make sense of their data and turn it into a strategic asset for smarter decision-making.

The rising demand for operational efficiency

The oil and gas sector involves complex, often hazardous operations that require meticulous planning and execution. Furthermore, human error, equipment failure, or inefficiencies in any part of the operation can result in significant financial losses or safety risks. In line with this, AI technologies, particularly machine learning (ML) and predictive analytics provide the capability to significantly optimize these operations. In addition, they can forecast equipment failures before they occur, automate repetitive tasks, and improve the precision of drilling and extraction processes. Moreover, AI not only reduces costs but also minimizes the risks associated with manual errors and system failures. As a result, operational efficiency is a major driving factor for the integration of AI in the oil and gas industry.

The growing emphasis on safety

The growing emphasis on safety in the oil and gas industry due to the hazardous nature of its operations, such as deep-sea drilling or working with highly flammable materials, is propelling the market growth. Furthermore, traditional safety measures often fall short of completely eliminating accidents and failures. In line with this, AI offers an advanced layer of safety protocols through real-time monitoring, predictive analytics, and automated control systems. It can analyze data from multiple sensors to detect irregularities that could signify a potential accident, enabling preventive actions to be taken before an incident occurs. Moreover, AI can automate certain high-risk tasks, reducing the need for manual intervention in potentially dangerous scenarios. As a result, the adoption of AI technologies for enhancing safety measures is a significant factor fueling the market growth.

AI in Oil and Gas Industry Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global AI in oil and gas market report, along with forecasts at the global, regional and country levels from 2024-2032. Our report has categorized the market based on type, function and application.

Breakup by Type:

Hardware

Software

Services

Software dominate the market

The report has provided a detailed breakup and analysis of the market based on type. This includes hardware, software, and services. According to the report, software represented the largest segment.

Software is dominating the market as it offers excellent flexibility and scalability, which make it highly adaptable to diverse operational needs. Furthermore, it can be easily updated to include new algorithms or features, ensuring that the oil and gas operations remain at the forefront of technological advancements. In addition, software solutions are more cost-effective in the long term, as they eliminate the need for heavy machinery or additional hardware installations. Besides this, it can be seamlessly integrated into existing systems, allowing for the centralization of data and processes. This harmonization significantly improves data analytics, enabling more accurate and timely decision-making. Moreover, software can be deployed across multiple sites, providing a unified approach to operations management. Apart from this, it can be continuously refined to address specific issues and opportunities presented by the oil and gas sector.

Breakup by Function:

Predictive Maintenance and Machinery Inspection

Material Movement

Production Planning

Field Services

Quality Control

Reclamation

Predictive maintenance and machinery inspection hold the largest share in the market

A detailed breakup and analysis of the market based on the function has also been provided in the report. This includes predictive maintenance and machinery inspection, material movement, production planning, field services, quality control, and reclamation. According to the report, predictive maintenance and machinery inspection accounted for the largest market share.

Predictive maintenance and machinery inspection are dominating the market as they aid in reducing downtime by analyzing equipment data and predicting failures before they happen. Furthermore, they help in identifying wear and tear or other forms of degradation that, if not addressed, could lead to serious safety issues. By preemptively identifying potential problems, companies can replace or repair components as needed, thereby improving the overall safety of operations. Additionally, the advancement in sensor technology and the Internet of Things (IoT), which has made data collection more robust and accurate, making predictive maintenance and machinery inspection increasingly reliable and effective, is positively influencing the market growth. Moreover, predictive maintenance and machinery inspection offers a strong return on investment (ROI), as they reduce maintenance costs, increase operational efficiency, and enhance security protocols.

Breakup by Application:

Upstream

Downstream

Midstream

Upstream hold the largest share in the market

A detailed breakup and analysis of the market based on the application has also been provided in the report. This includes upstream, downstream, and midstream. According to the report, upstream accounted for the largest market share.

The upstream is dominating the market as it involves various complex and data-intensive tasks, such as drilling, exploration, and extraction of natural gas and crude oil. Furthermore, it requires extensive data analysis for geological interpretation and reservoir modeling to identify promising drilling locations. In addition, AI-based predictive analytics are widely used in upstream operations to forecast equipment failures, allowing for preemptive actions that can save both time and money. Besides this, AI-powered remote sensing technologies and robotics are widely utilized to perform critical tasks that are either hazardous for human workers or logistically challenging to manage, thereby enhancing safety and operational efficiency. Moreover, the widespread adoption of AI in the upstream sector due to the imposition of strict environmental regulations is favoring the market growth.

Breakup by Region:

North America

United States

Canada

Asia-Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America exhibits a clear dominance, accounting for the largest AI in oil and gas market share

The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America has a well-developed infrastructure for both oil and gas extraction and AI technology, making it easier for companies to adopt and integrate AI solutions. Furthermore, the escalating level of investment in research and innovation by regional governments and private players to ensure continuous development and implementation of AI in the oil and gas sector is positively influencing the market growth. Besides this, companies in North America have a more mature understanding of the value of data analytics. This data-driven culture is conducive to the acceptance and optimization of AI capabilities across various sectors, including oil and gas. Moreover, the easy availability of a skilled workforce trained in data sciences and AI algorithms, which facilitates the implementation of advanced technologies, is contributing to the market growth.

Competitive Landscape:

Leading companies are leveraging machine learning (ML), predictive analytics, and natural language processing (NLP) to optimize every aspect of the oil and gas lifecycle, from exploration and drilling to production and distribution. Additionally, they are forging strategic partnerships with technology providers, academic institutions, and competitors to accelerate innovation and share knowledge. Furthermore, they are focusing on gaining customer insights to address specific problems and offer tailored solutions, which aids in building trust and improving overall customer satisfaction. Besides this, market leaders are investing in pilot programs to test the practical applications of AI technologies before full-scale implementation. Moreover, the escalating emphasis on sustainability and environmental responsibility has prompted companies to build AI solutions that meet the stringent regulatory requirements of various regions and countries.

The report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

Accenture plc

C3.AI Inc.

Cisco Systems Inc.

Cloudera Inc.

Fugenx Technologies

Huawei Technologies Co. Ltd

Infosys Limited

Intel Corporation

International Business Machines Corporation

Microsoft Corporation

Neudax

Nvidia Corporation

Oracle Corporation

Shell plc.

Recent Developments:

In January 2023, C3.AI Inc. launched the C3 Generative AI product suite, which will accelerate transformation efforts across business and industries, including oil and gas.

In March 2020, Accenture plc and SAP launched an upstream oil and gas solution to streamline processes and costs. This innovative solution uses AI to assist clients in increasing their visibility into operations and cash flow.

In October 2022, Huawei Technologies Co. Ltd showcased its Integrated Oil and Gas Field Network Solution, which uses edge computing, AI, and hard pipe isolation to manage all operations and enhance security.

Key Questions Answered in This Report

  • 1. What was the size of the global AI in oil and gas market in 2023?
  • 2. What is the expected growth rate of the global AI in oil and gas market during 2024-2032?
  • 3. What are the key factors driving the global AI in oil and gas market?
  • 4. What has been the impact of COVID-19 on the global AI in oil and gas market?
  • 5. What is the breakup of the global AI in oil and gas market based on the type?
  • 6. What is the breakup of the global AI in oil and gas market based on the function?
  • 7. What is the breakup of the global AI in oil and gas market based on the application?
  • 8. What are the key regions in the global AI in oil and gas market?
  • 9. Who are the key players/companies in the global AI in oil and gas market?

Table of Contents

1 Preface

2 Scope and Methodology

  • 2.1 Objectives of the Study
  • 2.2 Stakeholders
  • 2.3 Data Sources
    • 2.3.1 Primary Sources
    • 2.3.2 Secondary Sources
  • 2.4 Market Estimation
    • 2.4.1 Bottom-Up Approach
    • 2.4.2 Top-Down Approach
  • 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

  • 4.1 Overview
  • 4.2 Key Industry Trends

5 Global AI in Oil and Gas Market

  • 5.1 Market Overview
  • 5.2 Market Performance
  • 5.3 Impact of COVID-19
  • 5.4 Market Forecast

6 Market Breakup by Type

  • 6.1 Hardware
    • 6.1.1 Market Trends
    • 6.1.2 Market Forecast
  • 6.2 Software
    • 6.2.1 Market Trends
    • 6.2.2 Market Forecast
  • 6.3 Services
    • 6.3.1 Market Trends
    • 6.3.2 Market Forecast

7 Market Breakup by Function

  • 7.1 Predictive Maintenance and Machinery Inspection
    • 7.1.1 Market Trends
    • 7.1.2 Market Forecast
  • 7.2 Material Movement
    • 7.2.1 Market Trends
    • 7.2.2 Market Forecast
  • 7.3 Production Planning
    • 7.3.1 Market Trends
    • 7.3.2 Market Forecast
  • 7.4 Field Services
    • 7.4.1 Market Trends
    • 7.4.2 Market Forecast
  • 7.5 Quality Control
    • 7.5.1 Market Trends
    • 7.5.2 Market Forecast
  • 7.6 Reclamation
    • 7.6.1 Market Trends
    • 7.6.2 Market Forecast

8 Market Breakup by Application

  • 8.1 Upstream
    • 8.1.1 Market Trends
    • 8.1.2 Market Forecast
  • 8.2 Downstream
    • 8.2.1 Market Trends
    • 8.2.2 Market Forecast
  • 8.3 Midstream
    • 8.3.1 Market Trends
    • 8.3.2 Market Forecast

9 Market Breakup by Region

  • 9.1 North America
    • 9.1.1 United States
      • 9.1.1.1 Market Trends
      • 9.1.1.2 Market Forecast
    • 9.1.2 Canada
      • 9.1.2.1 Market Trends
      • 9.1.2.2 Market Forecast
  • 9.2 Asia-Pacific
    • 9.2.1 China
      • 9.2.1.1 Market Trends
      • 9.2.1.2 Market Forecast
    • 9.2.2 Japan
      • 9.2.2.1 Market Trends
      • 9.2.2.2 Market Forecast
    • 9.2.3 India
      • 9.2.3.1 Market Trends
      • 9.2.3.2 Market Forecast
    • 9.2.4 South Korea
      • 9.2.4.1 Market Trends
      • 9.2.4.2 Market Forecast
    • 9.2.5 Australia
      • 9.2.5.1 Market Trends
      • 9.2.5.2 Market Forecast
    • 9.2.6 Indonesia
      • 9.2.6.1 Market Trends
      • 9.2.6.2 Market Forecast
    • 9.2.7 Others
      • 9.2.7.1 Market Trends
      • 9.2.7.2 Market Forecast
  • 9.3 Europe
    • 9.3.1 Germany
      • 9.3.1.1 Market Trends
      • 9.3.1.2 Market Forecast
    • 9.3.2 France
      • 9.3.2.1 Market Trends
      • 9.3.2.2 Market Forecast
    • 9.3.3 United Kingdom
      • 9.3.3.1 Market Trends
      • 9.3.3.2 Market Forecast
    • 9.3.4 Italy
      • 9.3.4.1 Market Trends
      • 9.3.4.2 Market Forecast
    • 9.3.5 Spain
      • 9.3.5.1 Market Trends
      • 9.3.5.2 Market Forecast
    • 9.3.6 Russia
      • 9.3.6.1 Market Trends
      • 9.3.6.2 Market Forecast
    • 9.3.7 Others
      • 9.3.7.1 Market Trends
      • 9.3.7.2 Market Forecast
  • 9.4 Latin America
    • 9.4.1 Brazil
      • 9.4.1.1 Market Trends
      • 9.4.1.2 Market Forecast
    • 9.4.2 Mexico
      • 9.4.2.1 Market Trends
      • 9.4.2.2 Market Forecast
    • 9.4.3 Others
      • 9.4.3.1 Market Trends
      • 9.4.3.2 Market Forecast
  • 9.5 Middle East and Africa
    • 9.5.1 Market Trends
    • 9.5.2 Market Breakup by Country
    • 9.5.3 Market Forecast

10 SWOT Analysis

  • 10.1 Overview
  • 10.2 Strengths
  • 10.3 Weaknesses
  • 10.4 Opportunities
  • 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

  • 12.1 Overview
  • 12.2 Bargaining Power of Buyers
  • 12.3 Bargaining Power of Suppliers
  • 12.4 Degree of Competition
  • 12.5 Threat of New Entrants
  • 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

  • 14.1 Market Structure
  • 14.2 Key Players
  • 14.3 Profiles of Key Players
    • 14.3.1 Accenture plc
      • 14.3.1.1 Company Overview
      • 14.3.1.2 Product Portfolio
      • 14.3.1.3 Financials
      • 14.3.1.4 SWOT Analysis
    • 14.3.2 C3.AI Inc.
      • 14.3.2.1 Company Overview
      • 14.3.2.2 Product Portfolio
      • 14.3.2.3 Financials
    • 14.3.3 Cisco Systems Inc.
      • 14.3.3.1 Company Overview
      • 14.3.3.2 Product Portfolio
      • 14.3.3.3 Financials
      • 14.3.3.4 SWOT Analysis
    • 14.3.4 Cloudera Inc.
      • 14.3.4.1 Company Overview
      • 14.3.4.2 Product Portfolio
    • 14.3.5 Fugenx Technologies
      • 14.3.5.1 Company Overview
      • 14.3.5.2 Product Portfolio
    • 14.3.6 Huawei Technologies Co. Ltd
      • 14.3.6.1 Company Overview
      • 14.3.6.2 Product Portfolio
      • 14.3.6.3 SWOT Analysis
    • 14.3.7 Infosys Limited
      • 14.3.7.1 Company Overview
      • 14.3.7.2 Product Portfolio
      • 14.3.7.3 Financials
      • 14.3.7.4 SWOT Analysis
    • 14.3.8 Intel Corporation
      • 14.3.8.1 Company Overview
      • 14.3.8.2 Product Portfolio
      • 14.3.8.3 Financials
      • 14.3.8.4 SWOT Analysis
    • 14.3.9 International Business Machines Corporation
      • 14.3.9.1 Company Overview
      • 14.3.9.2 Product Portfolio
      • 14.3.9.3 Financials
    • 14.3.10 Microsoft Corporation
      • 14.3.10.1 Company Overview
      • 14.3.10.2 Product Portfolio
      • 14.3.10.3 Financials
      • 14.3.10.4 SWOT Analysis
    • 14.3.11 Neudax
      • 14.3.11.1 Company Overview
      • 14.3.11.2 Product Portfolio
    • 14.3.12 Nvidia Corporation
      • 14.3.12.1 Company Overview
      • 14.3.12.2 Product Portfolio
      • 14.3.12.3 Financials
      • 14.3.12.4 SWOT Analysis
    • 14.3.13 Oracle Corporation
      • 14.3.13.1 Company Overview
      • 14.3.13.2 Product Portfolio
      • 14.3.13.3 Financials
      • 14.3.13.4 SWOT Analysis
    • 14.3.14 Shell plc
      • 14.3.14.1 Company Overview
      • 14.3.14.2 Product Portfolio
      • 14.3.14.3 Financials