Despite its faults and inaccuracies in early iterations, there’s no denying that AI is transforming our daily lives at an incredible pace and most of the time the features, and broadly speaking, the benefits it offers are extremely useful.
But in terms of its ability to completely transform the energy industry (and specifically oilfield) economics it’s important to consider why much of the early AI conversation needs to be tempered with a degree of objectivity.
The reality is one of marginal gains in many areas - much like creating a good sports team, over time these gains add up rather than causing instantaneous results everywhere. And this is especially the case within the oilfield technology industry:
As a short historical background on AI’s components, Machine Learning was introduced relatively early, when Frank Rosenblatt introduced the first artificial neural network (ANN) in 1958. Two years later Bernard Widrow and Marcian Hoff used this new technology to create MADELINE, an ANN that could eliminate echo in phone lines, which is still in use today.
Data Mining was born much more recently during the late 1980’s and it focuses on the discovery of previously unknown facts hidden in data. The simplest of these are correlations: For example, if two data streams are correlated, it can be assumed they are linked through a cause and effect relationship (although this isn’t always true nowadays).
Some conversations in the energy industry suggest that AI can now transform everything in one giant leap. The fact is, as a highly valuable additional factor, it can and will provide significant incremental improvements. But it’s unlikely to radically transform everything overnight.
The successful cases in Oil and Gas will be those involving solid investments of engineering time and funding into the challenges faced. Decision makers also need to understand the difference and similarities in capabilities between the two specific areas within AI which are often confused - Machine Learning (ML) and Data Mining (DM). While similar theoretically, both employ the same methods and significantly overlap.
Alongside this consideration, the reality of AI in its current state is that it can be relatively complex to implement. In practice, large amounts of quality data are needed alongside time resource from very capable (and increasingly scarce) staffers to prepare that data. Characterising it appropriately and applying the different models can also make it expensive to implement in some cases.
Both Machine Learning and Data Mining do have huge potential to help us do things better, and examples already exist in practice within the oilfield arena. Again, it’s important to see these as "assistive technologies" in their current forms. Plugging them in to a data lake can’t solve all of the typical operational challenges we face – so we should also be wary of throwing out all the tools that currently serve their purpose effectively.
AI can already be easily used in combination with existing tools and first-principle models to enhance operational outcomes in our industry. It shouldn’t necessarily replace them, as many believe, and it’s worth noting that most successful examples of AI in effect today are in used in combination with other older technologies.
Artificial Intelligence is, by definition, not ‘real’ or ‘actual’ Intelligence. Just as artificial flowers from a distance look a lot like the real thing. Within this, ML is essentially about pattern recognition and it can be trained and refined to recognise patterns, so what we currently have is essentially "narrow AI". This involves carefully prepared data sets or trained ML models that specialise in solving a single particular problem. As a real-world example of this limitation, you can't take a model trained to drive a car and expect it to predict impending failure in a jet engine.
