Expanding Technology Frontiers in the Oil & Gas Industry

Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%.

The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment.

Some of the areas where AI technologies are being employed by the Oil and Gas industry are as follows:

  1. Planning and forecasting throughout the Exploration and Production (E&P) life-cycle
  2. Enhancing operational efficiency and reducing costs by real-time drilling optimization
  3. Land surveying and pipeline inspections
  4. Reducing risks using predictive maintenance

In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.

General Industry Trends

Figure 1: Patent publication trend [Click on image to enlarge]

The growth of patent publications related to AI technologies in the O&G industry shows a sharp increase since 2014, as seen in Figure 1. This corresponds to the increasing adoption of AI technologies for seismic surveying, usage of predictive modelling for data analysis, centralized integration of processes and other related areas.

Figure 2: Geographical distribution [Click on image to enlarge]


Figure 2 shows us that China is the largest jurisdiction where the applications are filed in this area, followed by the USA. Our analysis reveals that many of the emerging entities working in AI technologies for the O&G industry are based in China.








Companies leading in AI technologies in O&G

Figure 3: Portfolio growth [Click on image to enlarge]

Figure 3 shows the growth of the patent portfolios related to AI technologies in O&G for the top players. While Halliburton owns the largest number of patent assets in this area, with a sharp growth post-2014, GE exhibits the highest year-on-year growth in recent years.

Halliburton is one of the world’s largest providers of products and services to the energy industry. The company has been an early adopter of digital strategies to drive its business value. Halliburton entered into a partnership with Microsoft in the second half of 2017 to speed up its digital transformation.

Senior vice president of Halliburton Digital Solutions, Nagaraj Srinivasan commented that “Halliburton is at the forefront of the digital transformation occurring in the E&P industry. We believe open architecture and community-based innovation are necessary to drive this fundamental change and we’re proud to work closely with an industry leader like Microsoft to deliver tailored E&P digital business solutions to our customers across the globe.”

To bring the power of AI to the O&G industry, GE has teamed up with Nvidia in 2018. The partnership has provided GE with access to Nvidia’s entire range of AI-enabled tech, such as the Nvidia DGX-1 supercomputers, DGX Stations for desktop supercomputer capabilities, and the Nvidia Jetson AI, a platform for computing at the edge that enables deep learning processing locally.

Technology Trends

Figure 4: Evolution of technologies [Click on image to enlarge]

Figure 4 shows the evolution of AI-related technologies for O&G enterprises. The CPC code E21B 44/00 – Automatic control systems for drilling operation – forms the largest technology group and shows a sharp growth after 2015. Overall, the technologies covered by the CPC code G06N 20/00 – Machine learning – show the highest year-on-year growth post-2015.

Drilling is one of the high-risk operations in the O&G sector and requires huge investments as well. The implementation of AI in drilling helps in improved planning, real-time drill optimization, operational troubleshooting and risk identification and mitigation. Data from connected machines and predictive analytics using machine learning algorithms enable companies to improve infrastructure asset performance, optimize productivity and maintenance, and planning and forecasting.

Figure 5: Topic map [Click on image to enlarge]

Figure 5 is a Topic Map that shows the comparison of CPC codes for the company portfolios of Halliburton, Schlumberger, GE, Shell, and ExxonMobil. The size of each bubble represents the total number of patent applications across the company portfolios, and the coloured sectors represent the relative number of applications for the CPC code, in the respective portfolios of each patent holder. The bubble proximity corresponds to the “relatedness” of the individual CPC Codes.

It is observed that Halliburton possesses the largest patent portfolio for E21B 44/00 – Automatic control systems for drilling operation – related technologies, followed by Schlumberger. Schlumberger is one of the world’s largest oilfield services organizations that provide technology and services for oil and gas exploration, drilling, production and processing. Schlumberger has successfully incorporated AI across its E&P lifecycle.

A category-wise competitor comparison shows that GE leads in the areas of computational models and controlling parameters used for exploration and refining. Halliburton leads in AI-powered and self-operating equipment used for drilling. Read our complete report for all the details and additional insights.

The Road Ahead

A study of the patent assets held by the top O&G companies indicates that they are adopting AI, deep learning and predictive analytics to optimize their processes and monitor their regular functions.

Technologies such as machine learning and drill optimization are now commonplace within the industry. The early adopters of these technologies stand to gain the most in terms of increased efficiencies and an enhanced return on their R&D investments.  

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