Red Pill or Blue Pill?: The Impact of Fluid Separation Processes
Hello, and welcome back to the Cognitive Whiteboard. My name is Jim Ross and today I’m going to be talking about the impact of fluid separation processes. And I have gone for a Matrix theme with today’s whiteboard because I’m going to be asking you to make a choice, the blue pill or the red pill. If you take the blue pill, you wake up at your desk and you believe whatever your production technologists or reservoir engineers want you to believe. You take the red pill, you stay at the Whiteboard and I show you how deep the rabbit hole goes.
Now that I’m done with quoting The Matrix, what I’m actually going to talk about today is something which I and a number of clients have learned the hard way across our careers, and that’s that not all barrels of oil are created equal.
And to demonstrate that, let’s start off with a fixed mass, a fixed volume of reservoir-conditions oil. Now, if I flash that to standard conditions, I will end up with a certain set of properties associated with the two fluids that I get. There will be a gas-oil ratio and then the densities of those two phases. However, if I take the very same oil and put it through a different separation process – I have got an extra stage here – I will end up with a different set of properties. I will end up with a different gas-oil ratio and a different density between the two phases. Now, that seems reasonable enough. If I start with the same thing but put it through a different process, I will end up with a different result, but it’s not the only thing that can affect it. If we haven’t got tight temperature control, it can be affected by the time of day and how the temperature changes there or, on a longer time scale, we can also look at seasonal effect due to it being summer or winter.
Now, being a chemical engineer by training, I usually thought about things in mass balance. But when I joined the oil industry, that’s not how we operate. We think of things in terms of volumes. In particular, it’s usually standard-conditions volumes. And that’s what we use to report, that’s what we use for modelling and fiscal allocation: we tend to do on the basis of standard-conditions oil rates. But what we’ve seen here is, depending on the process I follow, I will end up with different properties and, therefore, in this case, a different rate off the back of that.
So what that means is that rate that we often hold to be gospel is anything but. The rate through path one and the rate through path two are not the same. Now, it’s quite common to have different paths. We might have a well-test path and we might have a field process path. That’s not uncommon. And the percentage difference might be quite small. But when we’re dealing with things on the scale of the reservoir, that can actually have quite a profound knock-on impact. The fluid properties we have already spoken about, they can have knock-on impact then, the fluid saturations and how they’ll respond to production and pressure changes, the well performance, multi-phase flow, the field development planning and well design. Are we designing for the correct path to surface? Are we using the correct rates?
History matching itself can be really complicated by this. If this separation process is changing over the life of the field, how is that being accounted for in the historical rates that we are now trying to match to? Basically, it leads to a very Matrix-like awakening where we suddenly find ourselves questioning everything we thought was real, every piece of production data that we have come across. Not all hope is lost, however. It is easy enough to convert between two paths just by looking at the shrinkage factor through both and then looking at that ratio and correcting appropriately. Now, the most robust way to do that is an equation of state. It’s also the most pernickety, and I could do a whole series of videos just on that process. So I’m not proposing that we go through how you do that but it’s more to question where those numbers have come from, and it’s okay to question them once we are armed with this knowledge. How are those rates measured? Were they measured at all? Were they done using allocation factors? If so, how were they calculated? Has this correction already been attempted? And if so, what was the physical basis for doing that?
So, basically, once we have this knowledge, we can take on any modeling challenge. We can use that sceptical attitude about where the numbers have come from to try and help us narrow down any difficulties we might have with our modelling. Once we do that, we can take anything on and we can make full use of our awakening to these possibilities.
I hope that’s been helpful. I look forward to seeing you at the Cognitive Whiteboard again in the future. Until then, take care.