Ensemble modeling workflows hold the potential to change the way we make decisions in subsurface projects. It is not enough to simply produce vast arrays of models, we need to unlock new and efficient ways of exploring these massive ensembles to find what matters most to our business outcomes. Luke is at the whiteboard explaining where we are going with this concept here at Cognitive Geology, and how you could get involved today
Hello and welcome back to "The Cognitive Whiteboard." My name's Luke and today, we're gonna talk about modeling ensembles and how they can help you understand the uncertainty of your assets and how that impacts your business decisions. So we've been talking in the industry about uncertainty for a very long time. And the reason for that being that whenever we make one interpretation of an oil field, given that we don't have an unlimited amount of data, any one interpretation has a degree of imprecision and a degree of inaccuracy. And so we recognize that when we make a single representation, it's only ever going to be precisely wrong. For many years, we've been exploring as an industry how to try to address that problem, and for some time we've been dealing with modeling variations in your input modeling parameters which allow you to explore the precision of your interpretations.
What we're gonna talk about with ensembles though, is how ensemble models can help you explore the accuracy. And as John Keynes here quoted quite a long time ago, "Accuracy is key to making a good quality decision." So when we're in a precision mode, we really got the underlying principles of geology kinda nailed down and we're exploring how variations in the other subtle parameters might change that answer. Modeling ensembles offers you the opportunity to greatly explore the uncertainty space, to take your skill sets as a geoscientist and bring that to describe alternative scenarios. It moves us more into this accuracy space. And it's better for us to be here. We would love to be precisely accurate, but that's just simply not possible in a typical oil field with the data that we have. But let's talk about how big a modeling ensemble can become.
We have the capability now, with cloud-based systems, to use elastic processing which basically means we can access an enormous number of simulations at once. How big could that be? Well, if we take a static example before we even get to the dynamics and look at how many different scenarios we could generate if each one of these properties was dependent upon the precursor, we start going up in the uncertainty space. So each one of these nodes, say where it's sort of seven or so of these, if we wanted to, say, take the percentiles out of all the deciles and we take that up to around here, we're going to be in a number of models that is simply impossible for you to manage cost effectively through a full flow simulation in compositional modeling. Now, there are plenty of cloud providers that would love you to do that, but you're probably going to spend your annual budget of the entire company in getting all of those systems run on the cloud.
So let's be more cost effective with it. Well here at Cognitive Geology, we have been working on that space for several years now. We have been developing methodologies that allow you to explore the complete uncertainty space in the entire modeling ensemble, without ever having to break your back in terms of budgets for running your simulations. And the way we're doing it, we're taking that entire modeling space, we're analyzing all of those scenarios and we're actually identifying before we do a lot of computation that a lot of the scenarios are basically similar. That means we can identify them as in the same area on that dart board, and so we can collapse them down. And what we've been doing is developing methodologies that allow us to reduce the number of scenarios that are moving through a progressively increased precision toolkit. So by the time we get down to our full compositional flow, we're able to identify the ones that actually matter that are going to change your business decisions.
What we're able to do with this is help you minimize your cost for software as a service and infrastructural cloud computing as a service, while still retaining the full accuracy of your asset. So we're launching a research consortium to further develop these methodologies that will be launched in London at the EAGE in June. We're also going to be talking, in a couple of technical talks, about how we've developed a few of these methods of the moment. So I'd love to see you there. If you're a company that's trying to figure out how you're gonna manage your budgets going forward with this cloud elastic computing, then come and have a chat with us because I think we can help you tremendously. Until then, if I don't see the EAGE, I'll see you back at "The Cognitive Whiteboard" next time.