Tuesday, 28 May, 2024 / Edition 9

We’re wrapping up this edition on Memorial Day, and the dichotomy of the holiday is striking. It’s an opportunity to reflect, mourn, and appreciate the members of our armed forces who have died securing the freedoms we enjoy. It is a somber day, but it also marks the beginning of summer, and I have a hard time believing most of the soldiers who died for us would not want us to celebrate the freedoms they died for alongside the promise of new life that our most vibrant season brings.

Dichotomies are all around us, and this week’s edition dives into the novel and exciting vs tried and true (some might say old and boring). So, grab a coffee, or maybe an ibuprofen to settle those Memorial Day…leftovers... and let’s dig in!

Sarah Compton

Editor, Enspired

Digital Twins vs. Models

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When I first read headline(s) about digital twins (DTs) gaining rapid acceptance in industry operations, my initial thought was, “Wow. Engineers are finally catching up to us geoscientists and deciding some models are useful.”

I realized I was conflating DTs and models, which got me wondering what the differentiator(s) between the two actually are. Are DTs simply a rebrand on models? Not really, as it turns out. 

Model vs. digital twin:

• A model is an approximation of a real thing/process we’re seeking to understand.

• One article tried to point out that DTs are transient, and I had to laugh because that’s not new. In geoscience—but apparently rarely in other industries—nearly all our models have time built into them. Even our engineer friends have transient models. They, of course, call them analyses, but they’re still basically models.

• The best differentiator I found is here, and it stated that a DT without a physical twin is a model. The key word here is “twin.” That’s not an “approximation.” That’s as close to an exact replica as possible.

Why use DTs? The purpose of a DT is to accurately predict changes over time, such as wear and tear of equipment, so that maintenance or replacements can come in prior to failure.

• A key component is the ability to collect accurate, meaningful, and real-time data that captures changes as they occur. DTs then deliver that data to those who can use it.

• Big benefits: The biggest ROI is seen in complex, multi-component assembled objects and products where some individual components or materials are expensive enough that reducing scrap is important.

SMEs, please: Geoscience has sometimes taken a backseat when an application seems heavy in engineering, but let’s not sleep on the fact that we need to establish our role in building and running DTs:

• When considering DTs involvement in CCS and renewable power projects, geologic information will be key.

• Estimates regarding carbon footprints require geoscience experts to understand inputs and mitigate outputs.

• We’re experts at managing the trade-offs between computational power, time, and realism in models.

Dive deeper: To see the full report that spurred the timing of this discussion, look here. There’s also a free summary here.

A message from AAPG Academy

Join AAPG Academy and S&P Global for an upcoming webinar on 4 June at 9am CDT where expert speakers from S&P Global, Shahab Siddiqui and Toby Burrough, will guide you through three techniques used to evaluate unconventional resources.

These techniques include:

• Decline Curve Analysis (DCA)
• Rate Transient Analysis (RTA)-based numerical modeling
• Data-driven Machine Learning

You'll walk away with an understanding of how geologists and engineers use these techniques to understand their unconventional resources and optimize assets for the future.

Is Newer Better?

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It’s an odd question for a tech and innovation newsletter, I know, but it’s an important one.

Evolution is to blame: There are some schools of thought that believe humans are inherently drawn to novelty based on our evolution.

• Until we had books, TVs, and the internet, our world was boring. Unless a stick and a hoop are your thing, I suppose.

• When we ventured out of our local environment (likely driven by our need to eat critters, which also wander), we got to encounter new things.

• When those new things didn’t kill us, we realized we were pleasantly stimulated.

Yes, but: As we all know, newer isn’t necessarily better.

• Our work is inherently dangerous, and new things are often untested, meaning they carry a higher risk profile to safety and effectiveness/usefulness.

• Depending on how “new” something is, it can also be more expensive.

• Sometimes, things labeled “new” are just a new coat of paint on an old house, a concern I mention earlier in this newsletter around digital twins.

But we need the new: Given the risks, it’s no wonder our industry can be slow to adapt new tech, but we find ourselves between a rock and a hard place—somewhere even geoscientists don’t like to be!

• While it’s rare to encounter a truly novel problem, geoscientists are faced with finding solutions to very difficult problems, and new technology often offers a glimpse at a solution.

• Desperation is rarely the way to find the best solution, so constantly keeping our eyes peeled for new tech is a great way to have things that could come in handy in our back pockets.

The bottom line: While we have to resist the urge to chase down new things simply because they’re new, we also need new things to chip away at previously unsolved problems.

We need to walk the line between satisfying our curiosity, a required trait to be good geoscientists, and satisfying our need for useful novelties. The two aren’t always the same.

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