A lot of industries across the world are witnessing a digital disruption of some sort. Digital frameworks are becoming a crucial part of operations. However, the pace of digital disruption in oil and gas industry has been noticeably slow. FactSet predicts that, at a sub-industry level, all four sub-industries in the oil and gas sector are projected to report a decline in earnings of more than 100%:
- Integrated Oil & Gas (-172%),
- Oil & Gas Exploration & Production (-160%),
- Oil & Gas Refining & Marketing (-143%), and
- Oil & Gas Equipment & Services (-110%).
This is of course not big news, considering the tumultuous (to say the least) start of the year the sector faced. The onset of these figures has already set the industry into a turmoil, oil prices plummeted months back which in turn left the industry still in losses. While the future can’t be predicted as the oil and gas industry is one of the most unpredictable sectors and also one of the most valuable industries, the industry has to now focus on sustainable and intelligent solutions. Unmistakably, Data Science and Artificial Intelligence can enhance the pace to achieve these desired outcomes.
Data Science and Artificial Intelligence in Oil and Gas
Considering the fact that the industry is a connected stream of sectors namely; Upstream, Midstream, and Downstream, it is important for oil and gas companies to leverage data analytics, machine learning, and AI to unlock new efficiencies in production while mitigating risk and reducing operational downtime.
- Leveraging AI and ML in the Upstream Sector: Machine Learning can be applied to point out the oil reserves and know the exact location where to drill by using the data of large volumes of seismic, soil, and equipment. Shifting from Condition-based maintenance to predictive maintenance so as to decrease the downtime failures and keep a check on healthy working conditions of the machines as well. Falling into the Production process, AI and ML can optimize oil extraction methods and accurately forecast production levels by analyzing seismic, drilling, well, and production data in real-time. Digitization can benefit exploration by using machine learning to reduce the time and cost of data interpretation by 50-60%. Field development will see savings in the engineering of up to 70%, with the addition of field concepts with a higher value. Furthermore, digital transformation will improve well delivery time and productivity by 20-30%.
- Leveraging AI and ML in the Midstream Sector: The direct impact of incorporating analytics eases out most of the internal and external challenges that one confronts in the midstream industry. Predictive analytics, with the help of historical data, certainly helps to avoid process deviations or failures by alerting the operators well in advance, which is otherwise a tricky pattern for a less experienced operator to notice. The transportation leading to the refineries no longer needs to be maintained with the help of human intervention. Sensors, what once could only detect oil pressure, speed and temperature can now detect the corrosion coefficient. Accurately modeled leak detection helps detect even the slightest of the leaks faster; resulting in quicker response, thus minimizing damage to the environment. Algorithms embedded with safety features alert operators on taking actions that violate hydraulic constraints.
- Leveraging AI and ML in the Downstream Sector: Integrate data from sensor networks, operational sources, enterprise systems, and external providers to power machine learning models that generate predictive insights across refineries, manufacturing, retail, and other downstream operations. This resolves critical issues such as reducing downtime, increasing operational efficiency, enhancing safety, and improving margins while generating $100s of millions in value annually.
Data Science and AI as a whole promise that the dynamics of the business can change for good, and we can have an intelligent outlook. The major three sectors are connected to one another and therefore a proper value proposed framework has to be established so as to achieve optimal growth with digital solutions. Advanced data science applications have a place in the oil and gas industry, and their potential to yield tangible benefits is considerably high. But unlike other industries, oil and gas face challenges ranging from the enormous complexity of drilling operations and the high cost of failure to the difficulty of obtaining the quantity and quality of data required for the development and improvement of machine learning algorithms. Nevertheless, innovation has always been at the core of the oil and gas industry, and many companies are already finding creative ways to deploy data science solutions. Applying the lessons learned from these successes within the right framework will allow AI to be deployed successfully across the industry.
Market Overview: Analyzing the real trends
- The AI in Oil and Gas market was valued at USD 2 billion in 2019 and is expected to reach USD 3.81 billion by 2025, at a CAGR of 10.96% over the forecast period 2020 – 2025.
- February 2020 – Royal Dutch Shell PLC has been expanding an online program that teaches its employees artificial intelligence skills, part of an effort to cut costs, improve business processes, and generate revenue.
- If the O&G Industry goes Paperless, the elimination of paper would also save about 800 million gallons of water for a benefit of $30 billion. Beyond that, customers would save about $170 billion and achieve productivity improvements worth another $10 billion.
- The threat of digital disruption was also a significant finding for the oil & gas industry. Only 19% of the respondents in oil & gas cited this factor as compared to 31% in other sectors. Outdated technology was a barrier for 24% of oil & gas executives, with 29% citing this factor in other sectors. A lack of support was a challenge for 25% of oil & gas execs.
- Microsoft announced the collaboration with energy industry tech company Baker Hughes and AI developer C3.ai to bring enterprise AI technology to the energy industry via its Azure cloud computing platform. It would allow customers to streamline the adoption of AI designed to address issues like inventory, energy management, predictive maintenance, and equipment reliability.
The truth is, most oil & gas businesses haven’t realized the full value of their investments in digitization. Less than one-fourth of the survey respondents indicated that they have realized more value than expected from their digital investments. Executives need to set clear goals for transforming their organizations if these investments are to pay for themselves. They also need to focus on developing the talent and leadership needed to make this happen.
(The views and opinions expressed in this article do not necessarily reflect the views of Energy Dais.)