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石化燃料人工智能

市場調查報告書

商品編碼

1407496

石油和天然氣市場中的人工智慧 - 2018-2028 年全球產業規模、佔有率、趨勢、機會和預測（按營運、服務類型、組件、地區、競爭細分）

AI in Oil and Gas Market - Global Industry Size, Share, Trends, Opportunity, and Forecast Segmented by Operation, Service Type, Component, By Region, By Competition 2018-2028

出版日期: 2023年11月07日 | 出版商:

TechSci Research | 英文 178 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

2022 年，全球人工智慧在石油和天然氣市場的價值為 33.7 億美元，預計在預測期內將強勁成長，到 2028 年CAGR為12.62%。石油和天然氣行業中的人工智慧透過預測幫助提高石油和天然氣產量維護和機械檢查、品質控制、住宅、探勘、儲罐和油藏監測等方法，並增加石油和天然氣行業的利潤。人工智慧由機器學習、人工神經網路、模糊邏輯和專家系統等各種工具組成，有助於將資料轉換為有用的資訊，然後將其應用於生命週期探索和生產的各個階段。石油和天然氣產業開始看到人工智慧對價值鏈各部門的令人難以置信的影響。人工智慧的機會直接打擊了當今油田面臨的最大挑戰。與其他缺乏對其油藏、營運流程和生產資產準確了解的營運商相比，有效利用人工智慧的公司預計將擁有明顯的優勢。

主要市場促進因素

降低成本

市場概況
預測期	2024-2028
2022 年市場規模	33.7億美元
2028 年市場規模	72.2億美元
2023-2028 年CAGR	12.62%
成長最快的細分市場	上游
最大的市場	北美洲

降低成本是推動人工智慧（AI）在全球石油和天然氣市場採用的主要驅動力。石油和天然氣行業以其資本密集而聞名，不斷尋求創新的解決方案來簡化營運並增強經濟活力。人工智慧技術在實現產業各個方面顯著降低成本方面發揮關鍵作用。人工智慧有助於降低成本的關鍵領域之一是營運效率。機器學習演算法即時分析感測器、鑽井活動和生產過程產生的大量資料集。透過識別這些資料中的模式和相關性，人工智慧系統可以最佳化營運工作流程，從而提高效率並減少資源浪費。由人工智慧支援的預測性維護是另一個重要方面，可以幫助操作員在設備問題升級為代價高昂的故障之前識別並解決它們。這不僅可以最大限度地減少停機時間，還可以延長設備的使用壽命，從而大大節省成本。

谷歌有限責任公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
IBM公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
FuGenX 技術列兵。有限公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
C3.AI
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
微軟公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
英特爾公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
荷蘭皇家殼牌公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services
俄羅斯天然氣工業股份公司石油公司
- Business Overview
- Key Revenue and Financials
- Recent Developments
- Key Personnel
- Key Product/Services

第 14 章：策略建議

第 15 章：關於我們與免責聲明

簡介目錄

Product Code: 20493

Global AI in Oil and Gas Market has valued at USD 3.37 Billion in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 12.62% through 2028. AI in Oil and Gas Industry aids in improving oil and gas output through predictive maintenance and machinery inspection, quality control, dwelling, exploration, tank and reservoir monitoring, and other methods as well as increases profit in the oil and gas industry. Artificial intelligence consists of a variety of tools such as machine learning, artificial neutral networks, fuzzy logic, and expert systems that aid in the transformation of data into useful information that can then be applied at various stages of the lifecycle's exploration and production. The oil and gas industry is beginning to see the incredible impact that AI can have on every sector in the value chain. The opportunities for AI strike directly at the greatest challenges in today's oilfield. Companies that effectively leverage AI are expected to have a distinct advantage over other operators that lack accurate understanding of their reservoirs, operating processes, and producing assets.

Key Market Drivers

Cost Reduction

Market Overview
Forecast Period	2024-2028
Market Size 2022	USD 3.37 Billion
Market Size 2028	USD 7.22 Billion
CAGR 2023-2028	12.62%
Fastest Growing Segment	Upstream
Largest Market	North America

Cost reduction is a primary driver propelling the adoption of Artificial Intelligence (AI) in the global oil and gas market. The oil and gas industry, known for its capital-intensive nature, is constantly seeking innovative solutions to streamline operations and enhance economic viability. AI technologies play a pivotal role in achieving significant cost reductions across various facets of the industry. One key area where AI contributes to cost reduction is operational efficiency. Machine learning algorithms analyze vast datasets generated by sensors, drilling activities, and production processes in real-time. By identifying patterns and correlations within this data, AI systems can optimize operational workflows, resulting in increased efficiency and reduced resource wastage. Predictive maintenance powered by AI is another crucial aspect, helping operators identify and address equipment issues before they escalate into costly failures. This not only minimizes downtime but also extends the lifespan of equipment, contributing to substantial cost savings.

Reservoir exploration and production optimization are also areas where AI-driven technologies significantly impact cost reduction. Advanced analytics and machine learning models enhance reservoir characterization and simulation, leading to more accurate predictions of reservoir behavior. This, in turn, enables operators to optimize production strategies, maximize recovery rates, and minimize unnecessary expenditures.

The deployment of AI in health, safety, and environmental initiatives further reduces costs associated with accidents, downtime, and regulatory non-compliance. By leveraging AI for risk prediction and mitigation, companies enhance workplace safety, reduce the likelihood of environmental incidents, and ensure compliance with stringent regulations.

Moreover, the integration of AI-driven automation and robotics in drilling and maintenance activities reduces dependency on human labor, particularly in hazardous environments. Autonomous drones and robots can perform routine inspections and tasks, minimizing operational risks and associated costs. In essence, the emphasis on cost reduction acts as a catalyst for the widespread adoption of AI in the oil and gas sector. Companies recognize that the implementation of AI technologies not only enhances efficiency and operational capabilities but also delivers a tangible impact on the bottom line, making it a strategic imperative for remaining competitive in a dynamic and challenging industry landscape.

Data Analytics and Insights

The global adoption of Artificial Intelligence (AI) in the oil and gas industry is significantly driven by the pivotal role of data analytics and insights. In an industry characterized by massive volumes of data generated from sensors, exploration activities, and production processes, AI-powered data analytics emerges as a transformative force. The ability of AI algorithms to sift through, process, and derive actionable insights from this vast data landscape is crucial for informed decision-making and operational optimization.

Data analytics in the oil and gas sector, powered by AI, brings forth a paradigm shift in reservoir exploration. Machine learning models analyze geological and geophysical data, providing a deeper understanding of reservoir characteristics. This enables companies to make more accurate predictions about reservoir behavior, optimizing drilling strategies and maximizing resource recovery. The result is not only increased operational efficiency but also significant cost savings. Real-time data analytics is instrumental in monitoring and managing drilling operations. AI algorithms process streaming data from drilling activities, identifying patterns and anomalies that might indicate potential issues. This proactive approach to data analysis allows for swift decision-making, reducing downtime and minimizing the risk of costly equipment failures. Predictive maintenance, a subset of data analytics, ensures that maintenance interventions are performed precisely when needed, preventing unnecessary disruptions and optimizing asset performance.

Beyond operational aspects, AI-driven data analytics contributes to health, safety, and environmental initiatives. By analyzing historical data, AI models can predict and prevent safety incidents, fostering a safer working environment. Environmental impact assessments and compliance monitoring benefit from the analytical capabilities of AI, ensuring adherence to regulatory standards. The significance of data analytics and insights in the oil and gas industry extends to market dynamics as well. AI facilitates market forecasting, helping companies make strategic decisions based on accurate predictions of supply and demand trends.

In essence, the integration of AI-driven data analytics and insights is a transformative force in the oil and gas sector, providing a competitive edge through enhanced decision-making, operational efficiency, and cost-effectiveness. As companies recognize the value of harnessing actionable insights from their data, the global AI in oil and gas market is poised for continued growth and innovation.

Key Market Challenges

Integration with Legacy Systems

The integration of Artificial Intelligence (AI) into the global oil and gas market faces a formidable challenge in the form of compatibility issues with legacy systems. Many companies within the industry operate with long-established infrastructure and technologies that were not originally designed to accommodate the advanced capabilities of AI. This mismatch between existing legacy systems and cutting-edge AI technologies poses a significant impediment to seamless integration, potentially hampering the widespread adoption of AI in the oil and gas sector.

Legacy systems, often characterized by rigid architectures and proprietary technologies, may lack the necessary interfaces and adaptability to effectively incorporate AI solutions. The integration process becomes complex, requiring meticulous planning and execution to ensure that AI systems can communicate with and complement existing infrastructure. Upgrading or replacing legacy systems altogether may be financially and operationally impractical for many companies, especially given the capital-intensive nature of the oil and gas industry. The challenge is twofold, encompassing both technical and cultural aspects. On a technical level, integrating AI with legacy systems requires a deep understanding of the existing architecture, data formats, and communication protocols. Legacy systems may not readily provide the standardized data formats and accessibility required for seamless integration with AI algorithms, leading to data interoperability challenges.

Culturally, there may be resistance to change within organizations accustomed to established workflows and technologies. Employees may require training to adapt to the new AI-driven processes, and there may be concerns about potential disruptions during the integration process.

Efforts to overcome the integration challenge involve developing robust middleware solutions that act as bridges between legacy systems and AI applications. These intermediary layers facilitate data exchange and communication, ensuring that AI technologies can leverage the data stored in legacy systems. Additionally, industry collaboration and knowledge-sharing can help establish best practices for integrating AI with diverse legacy architectures.

As the industry recognizes the transformative potential of AI in enhancing efficiency, decision-making, and overall operational excellence, addressing the integration challenge becomes crucial. Innovative solutions, collaborative approaches, and strategic planning are essential to successfully navigate the complexities of integrating AI into existing legacy systems within the oil and gas sector.

High Implementation Costs

The high implementation costs associated with adopting Artificial Intelligence (AI) in the global oil and gas market represent a significant hurdle that has the potential to impede widespread integration. The oil and gas industry, known for its capital-intensive nature, is often constrained by budgetary considerations, and the substantial upfront investments required for implementing AI technologies can be a deterrent. The integration of AI involves multifaceted expenses, including the acquisition of advanced hardware and software infrastructure capable of handling large-scale data processing, the hiring of skilled professionals, and ongoing maintenance costs. The need for specialized AI talent, such as data scientists and machine learning experts, adds to the financial burden, as these professionals command competitive salaries in a highly competitive job market. Additionally, companies may need to invest in comprehensive training programs to upskill existing employees, further contributing to the overall implementation costs.

For many oil and gas companies, particularly smaller and mid-sized enterprises, the high initial investment acts as a barrier to entry into the realm of AI adoption. This can result in a digital divide, with larger, more financially robust corporations reaping the benefits of AI-driven efficiencies while smaller players struggle to justify and afford the necessary investments. The result is a potential imbalance in competitiveness within the industry.

Moreover, the dynamic nature of AI technologies means that ongoing investments are essential to stay abreast of advancements and maintain the relevance of AI applications. Upgrading hardware, refreshing software, and adapting to evolving industry standards require additional financial commitments, making the total cost of ownership for AI implementations a long-term consideration.

To overcome the challenge posed by high implementation costs, industry stakeholders, including technology providers and government bodies, must collaborate to develop cost-effective solutions, promote research and development, and establish incentive programs to support AI adoption. Additionally, advancements in cloud-based AI solutions and innovative financing models may offer more accessible options for companies looking to integrate AI into their operations without the prohibitive upfront costs. Addressing the financial barriers to AI adoption is crucial for ensuring that the transformative potential of AI is realized across the entire spectrum of the oil and gas industry.

Lack of Skilled Workforce

The shortage of a skilled workforce stands out as a formidable challenge that has the potential to hamper the growth and implementation of Artificial Intelligence (AI) in the global oil and gas market. The successful integration of AI technologies into the industry requires a workforce with specialized expertise in data science, machine learning, and AI applications. Unfortunately, there is a notable scarcity of professionals possessing these specialized skills, creating a bottleneck for the widespread adoption of AI in the oil and gas sector.

The complexity of AI technologies demands a workforce that not only understands the intricacies of data analytics and machine learning algorithms but also possesses domain-specific knowledge of the oil and gas industry. This unique skill set is not readily available, and companies face challenges in recruiting and retaining talent with the necessary qualifications. The competition for skilled AI professionals is intense, with industries across the board vying for these experts, making it even more challenging for the oil and gas sector to attract and retain top-tier talent.

Furthermore, the rapid evolution of AI technologies requires continuous upskilling and training for existing employees within the industry. The lack of accessible and comprehensive training programs exacerbates the skill gap, hindering the ability of oil and gas companies to fully harness the potential of AI.

The consequences of a shortage of skilled professionals are multifaceted. Implementation of AI applications may be delayed, leading to missed opportunities for operational optimization, cost reduction, and enhanced decision-making. Companies may also face increased costs associated with outsourcing AI projects or hiring external consultants, further straining budgets. Addressing the lack of a skilled workforce in AI for oil and gas requires a concerted effort from educational institutions, industry associations, and companies themselves. Investing in training programs, fostering collaboration between academia and industry, and promoting STEM (Science, Technology, Engineering, and Mathematics) education are essential components of mitigating this challenge. As the industry recognizes the transformative potential of AI, bridging the skill gap becomes imperative for ensuring a sustainable and successful integration of AI technologies in the oil and gas sector.

Key Market Trends

Automation and Robotics

Automation and robotics, powered by Artificial Intelligence (AI), are poised to be major drivers propelling the global AI market in the oil and gas industry. This transformative synergy between AI and robotics is revolutionizing traditional operational processes, enhancing efficiency, safety, and overall productivity within the sector. In drilling operations, autonomous drilling systems equipped with AI algorithms are becoming increasingly prevalent. These systems can analyze real-time data, adjust drilling parameters, and optimize the drilling process, leading to improved precision and reduced drilling times. Routine maintenance tasks in the oil and gas industry are being reshaped by AI-driven robotics. Drones and robots, equipped with advanced AI capabilities, are deployed for inspections and maintenance activities in hazardous environments. These autonomous systems can navigate complex terrain, conduct thorough inspections, and execute necessary repairs, minimizing the need for human intervention in potentially dangerous situations. This not only enhances safety protocols but also contributes to cost savings by reducing downtime associated with maintenance activities.

Furthermore, AI-powered robotics play a crucial role in asset integrity management. Robots equipped with sensors and cameras can continuously monitor the condition of equipment and infrastructure, detecting anomalies or signs of wear. This proactive approach to asset management allows for early intervention and predictive maintenance, preventing costly failures and extending the lifespan of critical assets. The deployment of AI in automation and robotics aligns with the industry's goals of operational optimization, cost reduction, and adherence to stringent safety standards. It enables oil and gas companies to streamline operations, improve the precision and accuracy of tasks, and achieve higher levels of efficiency throughout the value chain.

As the industry continues to embrace digital transformation, the integration of AI-driven automation and robotics is expected to grow. This trend not only reflects a commitment to innovation but also underscores the industry's responsiveness to the evolving landscape and the need for sustainable and technologically advanced practices. Companies that invest in and leverage AI for automation and robotics are likely to gain a competitive edge, positioning themselves as leaders in the ongoing evolution of the global AI in the oil and gas market.

Predictive Maintenance

Predictive maintenance stands out as a driving force behind the evolution of the global AI in the oil and gas market. This strategic application of Artificial Intelligence (AI) is transforming the way the industry approaches equipment upkeep and operational reliability. By harnessing the power of machine learning algorithms, predictive maintenance analyzes vast datasets generated by sensors and equipment in real-time. The primary objective is to forecast potential equipment failures before they occur, enabling proactive maintenance interventions and minimizing downtime.

In the context of the oil and gas sector, where operational downtime can result in substantial financial losses, predictive maintenance powered by AI emerges as a game-changer. Machine learning models are trained on historical data, learning patterns and trends associated with equipment performance. This predictive capability allows operators to identify early signs of equipment degradation or malfunction, providing a window of opportunity for timely maintenance or replacement.

The implementation of predictive maintenance offers several key advantages. Firstly, it significantly reduces unplanned downtime, enhancing overall operational efficiency. By addressing issues before they escalate into critical failures, companies can optimize asset utilization, maximize production output, and extend the lifespan of equipment. This directly translates into cost savings and improved profitability for oil and gas enterprises.

Secondly, predictive maintenance supports a shift from traditional, calendar-based maintenance schedules to a more data-driven and condition-based approach. This means that maintenance activities are performed precisely when needed, reducing unnecessary interventions and minimizing the associated costs. This optimization of maintenance schedules contributes to a more efficient allocation of resources and manpower.

Moreover, the utilization of AI in predictive maintenance fosters a shift from reactive to proactive asset management strategies. Rather than responding to equipment failures as they happen, operators can take a preventative stance, avoiding disruptions and optimizing the overall reliability of operations. As the oil and gas industry continues to recognize the immense value of predictive maintenance, the global AI market in this sector is poised for substantial growth. Companies investing in AI-driven predictive maintenance solutions are not only enhancing their operational resilience but also positioning themselves at the forefront of innovation in a highly competitive industry landscape. The evolution towards predictive maintenance is indicative of the broader trend in leveraging AI for strategic decision-making and efficiency gains in the oil and gas sector.

Segmental Insights

Operation Insights

Upstream segment is expected to hold the largest share of AI in Oil and Gas Market for during the forecast period, Oil and Gas industries worldwide are trying to make the oil exploration processes more efficient and optimized. The operations in this field are the major factors driving the usage of AI in oil and gas companies. The AI tools can help oil and gas companies digitize records and automate the analysis of the gathered geological data and charts, which can lead to the potential identification of issues, such as pipeline corrosion or increased equipment usage.

Companies like BP and Royal Dutch Shell have planned to achieve net-zero carbon emissions by 2050 and are under increasing pressure to minimize their carbon footprint in compliance with the Paris Agreement. Shell is employing AI technology to do predictive maintenance of individual pieces of equipment or entire systems to reduce its carbon footprint. This factor allows the corporations to foresee and handle probable equipment faults before they occur.

Regional Insights

North America is expected to dominate the market 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 oil and gas market over the forecast period.

Factors such as the strong economy, the high adoption rate of AI technologies across the oilfield operators and service providers, a robust presence of prominent AI software and system suppliers, and combined investment by government and private organizations for the development and growth of R&D activities are projected to drive the demand for AI in the oil and gas sector in the region.

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 capacity is equivalent to an increase of nearly 80% compared to the present production capacity.

Key Market Players

Google LLC

IBM Corporation

FuGenX Technologies Pvt. Ltd

C3.AI

Microsoft Corporation

Intel Corporation

Royal Dutch Shell PLC

PJSC Gazprom Neft

Huawei Technologies Co. Ltd

NVIDIA Corp.

Report Scope:

In this report, the Global AI in Oil and Gas Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Global AI in Oil and Gas Market, By Operation:

Upstream
Midstream
Downstream

Global AI in Oil and Gas Market, By Service Type:

Professional Services
Managed Service

Global AI in Oil and Gas Market, By Component:

Solution
Services

Global AI in Oil and Gas Market, By Region:

North America
United States
Canada
Mexico
Asia-Pacific
China
India
Japan
South Korea
Indonesia
Europe
Germany
United Kingdom
France
Russia
Spain
South America
Brazil
Argentina
Middle East & Africa
Saudi Arabia
South Africa
Egypt
UAE
Israel

Competitive Landscape

Company Profiles: Detailed analysis of the major companies presents in the Global AI in Oil and Gas Market.

Available Customizations:

Global AI in Oil and Gas Market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

1. Product Overview

1.1. Market Definition
1.2. Scope of the Market
1.3. Markets Covered
1.4. Years Considered for Study
1.5. Key Market Segmentations

2. Research Methodology

2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Key Industry Partners
2.4. Major Association and Secondary Sources
2.5. Forecasting Methodology
2.6. Data Triangulation & Validation
2.7. Assumptions and Limitations

3. Executive Summary

4. Voice of Customers

5. Global AI in Oil and Gas Market Outlook

5.1. Market Size & Forecast
- 5.1.1. By Value
5.2. Market Share & Forecast
- 5.2.1. By Operation (Upstream, Midstream, Downstream)
- 5.2.2. By Service Type (Professional Services, Managed Services)
- 5.2.3. By Component (Solution, Services)
- 5.2.4. By Region
5.3. By Company (2022)
5.4. Market Map

6. North America AI in Oil and Gas Market Outlook

6.1. Market Size & Forecast
- 6.1.1. By Value
6.2. Market Share & Forecast
- 6.2.1. By Operation
- 6.2.2. By Service Type
- 6.2.3. By Component
- 6.2.4. By Country
6.3. North America: Country Analysis
- 6.3.1. United States AI in Oil and Gas Market Outlook
  - 6.3.1.1. Market Size & Forecast
    - 6.3.1.1.1. By Value
  - 6.3.1.2. Market Share & Forecast
    - 6.3.1.2.1. By Operation
    - 6.3.1.2.2. By Service Type
    - 6.3.1.2.3. By Component
- 6.3.2. Canada AI in Oil and Gas Market Outlook
  - 6.3.2.1. Market Size & Forecast
    - 6.3.2.1.1. By Value
  - 6.3.2.2. Market Share & Forecast
    - 6.3.2.2.1. By Operation
    - 6.3.2.2.2. By Service Type
    - 6.3.2.2.3. By Component
- 6.3.3. Mexico AI in Oil and Gas Market Outlook
  - 6.3.3.1. Market Size & Forecast
    - 6.3.3.1.1. By Value
  - 6.3.3.2. Market Share & Forecast
    - 6.3.3.2.1. By Operation
    - 6.3.3.2.2. By Service Type
    - 6.3.3.2.3. By Component

7. Asia-Pacific AI in Oil and Gas Market Outlook

7.1. Market Size & Forecast
- 7.1.1. By Value
7.2. Market Share & Forecast
- 7.2.1. By Operation
- 7.2.2. By Service Type
- 7.2.3. By Component
- 7.2.4. By Country
7.3. Asia-Pacific: Country Analysis
- 7.3.1. China AI in Oil and Gas Market Outlook
  - 7.3.1.1. Market Size & Forecast
    - 7.3.1.1.1. By Value
  - 7.3.1.2. Market Share & Forecast
    - 7.3.1.2.1. By Operation
    - 7.3.1.2.2. By Service Type
    - 7.3.1.2.3. By Component
- 7.3.2. India AI in Oil and Gas Market Outlook
  - 7.3.2.1. Market Size & Forecast
    - 7.3.2.1.1. By Value
  - 7.3.2.2. Market Share & Forecast
    - 7.3.2.2.1. By Operation
    - 7.3.2.2.2. By Service Type
    - 7.3.2.2.3. By Component
- 7.3.3. Japan AI in Oil and Gas Market Outlook
  - 7.3.3.1. Market Size & Forecast
    - 7.3.3.1.1. By Value
  - 7.3.3.2. Market Share & Forecast
    - 7.3.3.2.1. By Operation
    - 7.3.3.2.2. By Service Type
    - 7.3.3.2.3. By Component
- 7.3.4. South Korea AI in Oil and Gas Market Outlook
  - 7.3.4.1. Market Size & Forecast
    - 7.3.4.1.1. By Value
  - 7.3.4.2. Market Share & Forecast
    - 7.3.4.2.1. By Operation
    - 7.3.4.2.2. By Service Type
    - 7.3.4.2.3. By Component
- 7.3.5. Indonesia AI in Oil and Gas Market Outlook
  - 7.3.5.1. Market Size & Forecast
    - 7.3.5.1.1. By Value
  - 7.3.5.2. Market Share & Forecast
    - 7.3.5.2.1. By Operation
    - 7.3.5.2.2. By Service Type
    - 7.3.5.2.3. By Component

8. Europe AI in Oil and Gas Market Outlook

8.1. Market Size & Forecast
- 8.1.1. By Value
8.2. Market Share & Forecast
- 8.2.1. By Operation
- 8.2.2. By Service Type
- 8.2.3. By Component
- 8.2.4. By Country
8.3. Europe: Country Analysis
- 8.3.1. Germany AI in Oil and Gas Market Outlook
  - 8.3.1.1. Market Size & Forecast
    - 8.3.1.1.1. By Value
  - 8.3.1.2. Market Share & Forecast
    - 8.3.1.2.1. By Operation
    - 8.3.1.2.2. By Service Type
    - 8.3.1.2.3. By Component
- 8.3.2. United Kingdom AI in Oil and Gas Market Outlook
  - 8.3.2.1. Market Size & Forecast
    - 8.3.2.1.1. By Value
  - 8.3.2.2. Market Share & Forecast
    - 8.3.2.2.1. By Operation
    - 8.3.2.2.2. By Service Type
    - 8.3.2.2.3. By Component
- 8.3.3. France AI in Oil and Gas Market Outlook
  - 8.3.3.1. Market Size & Forecast
    - 8.3.3.1.1. By Value
  - 8.3.3.2. Market Share & Forecast
    - 8.3.3.2.1. By Operation
    - 8.3.3.2.2. By Service Type
    - 8.3.3.2.3. By Component
- 8.3.4. Russia AI in Oil and Gas Market Outlook
  - 8.3.4.1. Market Size & Forecast
    - 8.3.4.1.1. By Value
  - 8.3.4.2. Market Share & Forecast
    - 8.3.4.2.1. By Operation
    - 8.3.4.2.2. By Service Type
    - 8.3.4.2.3. By Component
- 8.3.5. Spain AI in Oil and Gas Market Outlook
  - 8.3.5.1. Market Size & Forecast
    - 8.3.5.1.1. By Value
  - 8.3.5.2. Market Share & Forecast
    - 8.3.5.2.1. By Operation
    - 8.3.5.2.2. By Service Type
    - 8.3.5.2.3. By Component

9. South America AI in Oil and Gas Market Outlook

9.1. Market Size & Forecast
- 9.1.1. By Value
9.2. Market Share & Forecast
- 9.2.1. By Operation
- 9.2.2. By Service Type
- 9.2.3. By Component
- 9.2.4. By Country
9.3. South America: Country Analysis
- 9.3.1. Brazil AI in Oil and Gas Market Outlook
  - 9.3.1.1. Market Size & Forecast
    - 9.3.1.1.1. By Value
  - 9.3.1.2. Market Share & Forecast
    - 9.3.1.2.1. By Operation
    - 9.3.1.2.2. By Service Type
    - 9.3.1.2.3. By Component
- 9.3.2. Argentina AI in Oil and Gas Market Outlook
  - 9.3.2.1. Market Size & Forecast
    - 9.3.2.1.1. By Value
  - 9.3.2.2. Market Share & Forecast
    - 9.3.2.2.1. By Operation
    - 9.3.2.2.2. By Service Type
    - 9.3.2.2.3. By Component

10. Middle East & Africa AI in Oil and Gas Market Outlook

10.1. Market Size & Forecast
- 10.1.1. By Value
10.2. Market Share & Forecast
- 10.2.1. By Operation
- 10.2.2. By Service Type
- 10.2.3. By Component
- 10.2.4. By Country
10.3. Middle East & Africa: Country Analysis
- 10.3.1. Saudi Arabia AI in Oil and Gas Market Outlook
  - 10.3.1.1. Market Size & Forecast
    - 10.3.1.1.1. By Value
  - 10.3.1.2. Market Share & Forecast
    - 10.3.1.2.1. By Operation
    - 10.3.1.2.2. By Service Type
    - 10.3.1.2.3. By Component
- 10.3.2. South Africa AI in Oil and Gas Market Outlook
  - 10.3.2.1. Market Size & Forecast
    - 10.3.2.1.1. By Value
  - 10.3.2.2. Market Share & Forecast
    - 10.3.2.2.1. By Operation
    - 10.3.2.2.2. By Service Type
    - 10.3.2.2.3. By Component
- 10.3.3. UAE AI in Oil and Gas Market Outlook
  - 10.3.3.1. Market Size & Forecast
    - 10.3.3.1.1. By Value
  - 10.3.3.2. Market Share & Forecast
    - 10.3.3.2.1. By Operation
    - 10.3.3.2.2. By Service Type
    - 10.3.3.2.3. By Component
- 10.3.4. Israel AI in Oil and Gas Market Outlook
  - 10.3.4.1. Market Size & Forecast
    - 10.3.4.1.1. By Value
  - 10.3.4.2. Market Share & Forecast
    - 10.3.4.2.1. By Operation
    - 10.3.4.2.2. By Service Type
    - 10.3.4.2.3. By Component
- 10.3.5. Egypt AI in Oil and Gas Market Outlook
  - 10.3.5.1. Market Size & Forecast
    - 10.3.5.1.1. By Value
  - 10.3.5.2. Market Share & Forecast
    - 10.3.5.2.1. By Operation
    - 10.3.5.2.2. By Service Type
    - 10.3.5.2.3. By Component

11. Market Dynamics

11.1. Drivers
11.2. Challenge

12. Market Trends & Developments

13. Company Profiles

13.1. Google LLC
- 13.1.1. Business Overview
- 13.1.2. Key Revenue and Financials
- 13.1.3. Recent Developments
- 13.1.4. Key Personnel
- 13.1.5. Key Product/Services
13.2. IBM Corporation
- 13.2.1. Business Overview
- 13.2.2. Key Revenue and Financials
- 13.2.3. Recent Developments
- 13.2.4. Key Personnel
- 13.2.5. Key Product/Services
13.3. FuGenX Technologies Pvt. Ltd
- 13.3.1. Business Overview
- 13.3.2. Key Revenue and Financials
- 13.3.3. Recent Developments
- 13.3.4. Key Personnel
- 13.3.5. Key Product/Services
13.4. C3.AI
- 13.4.1. Business Overview
- 13.4.2. Key Revenue and Financials
- 13.4.3. Recent Developments
- 13.4.4. Key Personnel
- 13.4.5. Key Product/Services
13.5. Microsoft Corporation
- 13.5.1. Business Overview
- 13.5.2. Key Revenue and Financials
- 13.5.3. Recent Developments
- 13.5.4. Key Personnel
- 13.5.5. Key Product/Services
13.6. Intel Corporation
- 13.6.1. Business Overview
- 13.6.2. Key Revenue and Financials
- 13.6.3. Recent Developments
- 13.6.4. Key Personnel
- 13.6.5. Key Product/Services
13.7. Royal Dutch Shell PLC
- 13.7.1. Business Overview
- 13.7.2. Key Revenue and Financials
- 13.7.3. Recent Developments
- 13.7.4. Key Personnel
- 13.7.5. Key Product/Services
13.8. PJSC Gazprom Neft
- 13.8.1. Business Overview
- 13.8.2. Key Revenue and Financials
- 13.8.3. Recent Developments
- 13.8.4. Key Personnel
- 13.8.5. Key Product/Services