The Bigger Picture: Uncertainty from Subsurface to Separator


Hello and welcome back to the Cognitive Whiteboard. My name is Jim Ross. I’m the new product owner of Hutton here at Cognitive Geology. And today I’m going to be talking to you about “The Bigger Picture: Uncertainty from Subsurface to Separator.” My background is chemical engineering and most recently, I’ve been working as a petroleum engineer in the field of integrated production modeling. What that means is taking models of all the different parts of our oil and gas system and constructing a model that represents the entire behavior of the system and how they’re going to interact and affect each other once we start producing from that asset.

And what you notice when you’re doing this is that everybody in the oil industry kind of works in their own little silos. You might have production engineers who are responsible for the operation design of the wells, you might have a drilling team who decide where and when we’re going to be actually drilling those wells, a facilities team who would look at things such as the process or refinery required to support that production and anything else that’s required to actually make that happen, and then the reservoir model, which of course, would be looking at how the fluids are actually going to move through the porous medium of the reservoir rock. However, they’re all working towards a common goal, which is usually something like how much oil am I going to produce and when am I going to produce it.

And in creating these models, I would often get asked, “How do I know this is correct? How do I know that what we’ve predicted is what will actually happen? And the short answer is, we don’t. We don’t know that that’s entirely correct. And the reason for that is because of all the various uncertainties we might have in the process of building these models. For instance, do I have a reliable lab report? Do I know what my fluid density is, my API gravity, gas gravity is going to be? Do I know what the ratio is going to be of gas and oil and even how much water I’m going to produce over time? Do I know what the drainage region of my well is going to be and how much of that I’m actually going to contact during production and what that pressure decline is going to be, even how many wells I’m going to have and what type they’re going to be and how it’s going behave if we’re not operating at the design capacity of those facilities?

And what that leads to is some very anxious engineers, which is why I can have sympathy with this guy here. So, something that we’ve tried to move towards is to look less at precisely what these inputs would be and look at what impact they have by looking at the different possibilities on the production side. So for instance, a production engineer might sensitize on tubing size to see how much the well is going produce and what effect that will have on the production. If we’re looking at a perforation, we might look at the perforation efficiency, how well we’re making those perforations and what impact that would ultimately have on any wells that we drill. If we undertake a stimulation job, then we might look at what the stimulated PI would be after.

There’s a range of possibilities for what we might end up with there. What we’re trying to build here at Cognitive Geology is something that takes into account the geological possibilities. So what are the different possibilities for filling my rock properties across the entire grid? And what impact does that have on the process? What that allows us to do is to move away from something which is precise, but we don’t necessarily have a lot of confidence in, to something that is approximately accurate that then tells us what the impact of our decisions and the impact of our unknowns would be so we can have greater confidence in what we’ve predicted going forward. That’s all I want to talk to you about today but I look forward to seeing you at the Cognitive Whiteboard again in the future, and I’ll see you then.

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