Artificial intelligence refers to the ability of devices to learn and operate without human intervention. The AI platform uses a set of predictive and prescriptive analytics tools to generate solutions to events on the basis of several variables more accurately. AI in industries with high volatility and risk such as oil and gas can lead to savings worth billions. From managing effective power consumption to predicting oil and gas productions trends, AI has developed capabilities to aid human decision making, thereby helping companies to withstand changing market dynamics.
FIGURE 1 AI architecture in oil and gas. Source: Secondary Research
The Journey so far
Currently, AI is being used for predicting equipment failure and scheduling maintenances in oilfields. This has reduced the non-productive time (NPT) due to equipment failure, especially for drilling operations. NPT during drilling operation cost around 32% of the overall drilling expenses. Due to the recent downturn in the oil and gas industry, drilling operators have experienced a major setback. Drilling companies are now leveraging the power of artificial intelligence to evaluate the reserves and avoid any chances of unplanned downtimes. During the drilling of wells, the decision-making time related to blasting and faster drills is very critical. This can result in improved productivity and reduced operational costs. Therefore, drilling companies are readily adopting AI technology as a part of their suite or solution. Moreover, due to lesser human intervention, several accidents can be avoided. For instance, IP cameras with AI can monitor and avoid disasters caused by human negligence, such as workers smoking within the rigs. These cameras use facial recognition and object identification technologies to detect the threat accurately.
In addition to this, oilfield operators aim to achieve low production cost with the implementation of AI technology. Using AI can help in reducing the risk of uncertain events leading to loss of production and in maintaining production in line with the market demand.
FIGURE 2 COST OF PRODUCING A BARREL OF OIL & GAS, 2016
AI in the oil & gas market is estimated to be USD 1.5 billion in 2017 according to a research study published by MarketsandMarkets Research Pvt. Ltd. The Software segment is likely to be the frontrunner with more digital oilfields adopting AI and ML technologies to cope with the changing dynamics of the industry. This will also lead to the development of better asset management capabilities from various solutions providers.
The frontrunners: Who is ready for AI?
In recent years, there have been major developments in the area of AI solutions and related software development kits. Companies such as Alphabet, Microsoft, Siemens, IBM, Cisco, and Infosys are among the leading players in the development of AI solutions in the Oil & Gas market. These players adopted various strategies such as partnerships & collaborations and new product development. Partnerships helped the companies to develop better asset management capabilities, which can help the companies offer complete solutions.
Operator companies such as Shell, Total, and Exxon Mobil have been introducing several initiatives involving artificial intelligence. Shell is using AI platforms to drive predictive maintenance and is expanding the application of AI across the company. According to Wall Street Journal, the company aims to use AI along with edge analytics to maintain compressors, valves, and other equipment; steer drill bits through shale deposits; and improve the safety of employees and customers.
| Rank | AI Suppliers | Key Developments |
| 1 | IBM | IBM partnered with the Massachusetts Institute of Technology (MIT) to expand machine learning capabilities
IBM collaborated with Repsol to leverage cognitive technology for the oil & gas industry |
| 2 | Accenture | Accenture opened a new lab in Dublin, Ireland, to expand its AI capabilities and R&D services.
Accenture launched Accenture Cyber Intelligence Platform |
| 3 | Google (Alphabet) | Google partnered with Facebook, IBM, and Microsoft, to advocate AI-based solutions and create public awareness with regard to AI-based solutions.
Google partnered with Spark cognition to accelerate the development and deployment of its proprietary AI products |
| 4
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Microsoft | Microsoft collaborated with Halliburton to drive digital transformation across the Oil & Gas industry.
Microsoft planned to expand its AI business by launching a new research group, Microsoft Research AI. |
Source: Company Annual Reports
Apart from these players, companies such as FugenX Technologies, Numenta, Cisco, and Hortonworks are aggressively pursuing AI as an integral part of their offering, making it ready for future demand.
Future trends and investments
In midstream, trespassing and intrusions are avoided using similar techniques. During the transportation of produced fluids, pipelines are the most preferred mode. These pipelines face harsh conditions and are built through remote areas. In order to safeguard the fluid transportation, asset monitoring plays a vital role. Asset monitoring combined with AI and ML technology can result in faster detection of anomalies and can save billions. In the downstream sector, on the other hand, production planning and refined product logistics are also better handled using AI and ML.
The integration of AI in other processes in oil and gas is expected to be one of the mega trends in the next 10 years. With operators investing more on AI, they can predict the shifts in oil prices to manage business decisions accordingly. Moreover, AI combined with digital twin platforms can lead to real-time simulation and modeling of the system, which, in turn, can help in virtually tackling all possible outcomes associated with the processes in oil and gas industry.
North America is expected to continue to lead the global AI in the Oil & Gas market and is projected to grow at a CAGR of 12.85% during the forecast period. This growth is due to the increasing adoption of AI technologies by oilfield operators & service providers and higher investment in joint ventures and start-ups for AI in Oil & Gas. The market in the Asia Pacific is projected to be the second largest during the forecast period, with a projected market size of USD 2.9 billion by 2022.
Cyber attacks – A challenge for the energy industry
Cybersecurity is the prime concern of stakeholders in the oil & gas industry. Thousands of miles of pipelines mileage are vulnerable to cyber attacks. A cyber security agency has claimed to witness around 170 attacks per day on pipeline operators. This has increased the need for securing the large chunks of datasets and control centers from such elements. Earlier, oilfield operators were under the impression that oilfields are safer compared to midstream and downstream sectors. However, Brian Walker, a former head of Marathon Oil Corp.’s global IT, in an interview with Bloomberg said that “the real challenge is that we play ostrich turning blind eyes towards such incidents until it is too late to prevent them.” In contrast to this, operators see this as an additional expense in this low oil price scenario. Hence, they have set themselves vulnerable to such attacks.
Addressing security issues in the industry is tough without the participation from the stakeholders, such as oilfield operators, pipeline operators, and equipment & service providers. The best resolution to this would be enhancing the threat of intelligence and investing adequately in cybersecurity.
What lies ahead
No industry, including the oil & gas industry, has fully utilized the potential of artificial intelligence. The collaborative usage of predictive and edge analytics for various processes in the industry can lead to safer and efficient operations. The direction that we are heading towards has information as the controlling power and collaborative operations as the future of the industry. Considering the adoption of this latest technology is likely to be the first step from all the solution providers. The easiest and the most anticipated being edge analytics. Edge analytics allows data to be analyzed and processed at its origin itself, thereby reducing the response time and mitigating malfunctions even before they occur. Implementing such solutions across the industry can really help in the efficient handling of situations such as the industry downturn that started in 2014.
