The oil and gas industry has always been quick to adopt technological advancements, from early drilling methods to advanced seismic techniques, constantly pushing the boundaries of innovation to maximize production efficiency and profitability. Now, with Machine Learning (ML) and Artificial Intelligence (AI) taking center stage, the industry is once again poised for a major shift. The question on everyone’s mind, will Machine Learning and AI replace reservoir engineers?
In this blog, we’ll dive into this topic and explore how AI/ML could impact the role of reservoir engineers. We’ll examine whether they pose a threat or offer complementary benefits to engineers. We’ll also discuss the ideology of Machine Learning and AI, including why some believe it’s here to replace us and the implications this has for the oil and gas industry. Whether you’re a reservoir engineer or simply curious about the future of the industry, this blog is for you. So, keep reading to find out more about the potential impact of Machine Learning and AI on the oil and gas industry.
I. The ideology of machine learning
It may seem counterintuitive, but throughout history, there have been many examples of humans fearing that technology could replace their jobs. When ATMs were first introduced, people believed they would eliminate the need for bank tellers. However, this was not the case, and new job roles emerged in the banking industry. Similarly, when email became widely used, some people feared that it would lead to the end of the postal service and postal workers’ jobs. Yet, this didn’t happen, and new job in digital communications and marketing emerged.
Although it is true that AI will eventually replace some jobs and industries, it will also generate millions of employment opportunities in emerging fields in the future. As with most technological advancements, the transition from human labor to automation must be gradual to ensure that AI technology can perform job tasks more effectively than humans.
In the oil & gas industry, It’s interesting to see how engineers react differently to the concept of machine learning. Some embrace it as the next big thing, while others feel uneasy about it. I believe that some of the discomfort stems from the notion that machines are being created to replace human labor, a concept that was instilled in us through education and reinforced by the media.
When you hear the term “machine learning,” it’s easy to picture a world where computers are doing all the work and humans are left out in the cold. But that’s not accurate. While machine learning can certainly automate certain tasks, it’s not meant to replace human workers altogether. Of course, there are always those who take things too far. Some people think that machines can learn to do anything if we just give them enough data. Even though machine learning can identify patterns that may escape human detection, it still faces challenges in decision-making and interpreting complex behaviors.
While J.A.R.V.I.S. was created to assist Iron Man in making informed decisions, machine learning can aid reservoir engineers in making more precise forecasts and informed choices. However, it is crucial to recognize that the engineers hold the ultimate responsibility for the outcomes, much like Iron Man. Despite providing valuable data and insights, machine learning cannot substitute the vital human qualities of critical thinking, creativity, and expertise. So, while it’s important to be aware of the potential impact of machine learning on the workforce, we shouldn’t be afraid to embrace this exciting new technology.
II. Machine learning & Oil & Gas Engineers: Substitution or complementary relationship?
The growing hype surrounding AI, machine learning, and big data has been making waves in the oil and gas industry. However, the rise of these technologies has also instilled a sense of fear among petroleum engineers, who worry that their jobs may become obsolete in the face of automation. This fear is understandable but it’s important to note that automation is not meant to replace workers. Instead, these tools are designed to augment and enhance the work done by engineers.
1- Substitution or complementary relationship?
It’s interesting how engineers are more concerned about competing with computers rather than humans, don’t you think? I mean, computers are so different from us in so many ways. They can do some things better than us, but we have intentionality and can think creatively in complicated situations.
Machine learning is great at figuring out relationships hidden within masses of oil & gas data. What this really means is we’re seeing the increasing automation of the highly repetitive, boring, and tedious tasks of pulling data from different sources and looking for signals within the noise. This is where machines, rather than people, excel. But even with all this power, ML struggles to make basic judgments that would be simple for any human.
So, will reservoir engineers be needed once these massive computers are crunching billions of data points to make accurate forecasts for critical assets, acquisitions, or development studies? The answer is yes…and no. This depends on whether you embrace technology or decide to be left behind. These technologies are more likely to work alongside reservoir engineers and enhance their work if they possess the right skills to leverage machine learning technology.
But one thing for sure – the most valuable E&P companies in the future won’t see technology as a threat, but rather as a hyper specialized partner who will empower its engineers to make better development & investment decisions.
2- Understanding automation potential
In this article, McKinsey has conducted an analysis of all activities based on their potential for automation. When we talk about potential, we are referring to the likelihood that a specific activity could be automated by adopting new technologies. According to the graph presented below, activities such as data collection, data processing, and repetitive/physical work exhibit a high susceptibility to automation.
The field of reservoir engineering is facing a potential disruption due to the rapid advancements in technology. With the increasing proficiency of algorithms in analyzing vast amounts of data from multiple sources and creating precise models to forecast reservoir behavior and improve production, many tasks currently performed by reservoir engineers might become automated.
However, it’s unlikely that these technologies will completely replace reservoir engineers because they possess unique skills and knowledge that are essential to the field. This is why we see “applying expertise” in the less susceptible area in the McKinsey graph. For example, engineers have a deep understanding of the geological and geophysical aspects of reservoirs, as well as the technical and economic factors that impact reservoir management and optimization. Moreover, they can make complex decisions based on incomplete or uncertain information, a skill that machine learning and AI find challenging.
III. What does the future hold?
As the oil and gas industry continues to adopt machine learning and AI, industry experts suggest that reservoir engineers must stay ahead of the curve by learning how to work with these technologies. “There’s no doubt that AI will help manage engineering data more efficiently and will be an essential component of engineering’s future. The sooner it’s adopted and adapted to; the sooner engineering will be able to capitalize on the advantages of the technology.” – Forbes (https://www.forbes.com/sites/bernardmarr/2020/02/07/how-is-artificial-intelligence-and-machine-learning-used-in-engineering/)
That being said, even if you are a reservoir engineer, it is important to acknowledge that technological advancements and changes in the job market can still have an impact on your career. However, the good news is that the transition to automation is likely to be gradual, rather than sudden. This provides an opportunity for individuals to upskill and stay relevant in the field by learning how to work with and leverage machine learning technology.
By developing the ability to analyze data, create models, and optimize production using machine learning algorithms, reservoir engineers can provide invaluable insights to increase efficiency and reduce costs for their companies. Moreover, it is worth highlighting that reservoir engineers who possess data science skills and can work with machine learning technology will be in high demand. They will be able to provide unique value and support E&P companies in optimizing production and reducing costs, creating a significant incentive for individuals to acquire new skills and embrace the potential of machine learning technology.
Where to start to stay relevant?
Here are some ways to start acquiring the necessary skills to stay relevant as a reservoir engineer in the age of machine learning:
- Attend training programs and workshops: Novi’s technical papers library
- Follow data scientists and thought leaders in the space: e.g. Ted Cross
- Experiment with machine learning tools like Novi Labs
- Join industry-specific online communities (Exciting things are brewing, and we’ll be unveiling them shortly. Stay tuned for the big reveal!)
By taking these steps, reservoir engineers can position themselves to stay relevant and provide unique value in the industry.
In summary, the oil and gas industry is no stranger to adopting technological advancements, and machine learning and AI are no exception. The influence of machine learning technology on reservoir engineering is becoming more evident. Reservoir engineers who can’t adjust to this quickly changing landscape may face the risk of being replaced by machines. However, those who can work with and take advantage of machine learning technology will remain important and valuable in the industry. Reservoir engineers can position themselves for success in an ever-evolving industry by embracing this technology and acquiring new skills. This may include developing expertise and knowledge related to these technologies, collaborating more closely with data scientists and other machine learning and AI specialists in the field.