We're at the Cognitive Whiteboard again with Luke, discussing simulation sprints, and how a lean and iterative approach can provide robust business recommendations, in a fraction of the time taken to build a “perfect” model.
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Simulation Sprints: Minimal Cost, Maximum Value
Hello and welcome back to the Cognitive Whiteboard. My name's Luke and today we're talking about simulation sprints. This isn't a technical workflow, it's a project management one. But it's something that's completely revolutionised the way I do my work and I'd like to share with you how this can help you make much more cost-effective analyses of your reservoirs in a vastly shorter period of time.
Now the method is not something that I claim credit for because it was challenged to me by a person called Michael Waite in Chevron who once came to me with a new field to look at, a mature field with a lot of injection and production and asked me to give him a model by the end of the day. My reaction to that was shock - I guess would be the polite way of putting it - because that's obviously, an impossible ask for you to try to build a reservoir model and understand what's going on in a complex brown field in just a matter of a single day.
But Michael wasn't being silly. He was challenging me to use a methodology that would allow us to make quick and effective decisions when we clearly knew what the business decision that was coming up was going to be. What we had in this particular case is a mature field with only about 12 or so locations left for us to drill, and six well slots that we needed to fill. We had wells that were declining in production and we were going to replace them. And so, there really wasn't any other choice other than optimizing those well, bottom-hole locations.
And so, in that context you can come back and say well, we don't necessarily need to answer the ins and outs of the entire reservoir but rank and understand those locations so that we can drill the optimum ones during the drilling campaign. And he introduced to me this concept of simulation sprinting. What it is, is a quick loop study that you can do numerous times, iterating through progressive cycles of increasing precision until you get to a point of accuracy that allows you to make a valid and robust business recommendation.
The first one in a single day, we were not going to be able to build a realistic reservoir model by any means. What we were able to do in a single day is do some pretty decent production mapping results. So, taking the last six months of production looking at the water cut, we got together with the production mapping team. And we were able to design a workflow that we could do that day that would give us an idea of what was going to address this bigger objective, and try to say what would be the lowest water cut because that's another value measure that we could use to understand these wells.
Importantly, because we're gonna do this a lot, even though we call it sprints, the key is to work smart, not hard, because it's gonna keep going on over time. So, you wanna be able to do this within the normal working hours. Don't burn the midnight oil, otherwise, you'll burn out before you get to make your robust business decision.
The really important piece in this cycle though is the number five. When we come to assess the outcomes of any one of the experiments that we've done, we need to rank the wells that we had in order of economic value. So, whatever way we were trying to devise it, we needed to have those well targets ranked from best to worst at the end of each one of these simulations sprints. That's what Mike was asking me for at the end of the day.
And when we do this assessment, we also spend time to have a look at what's the worst part of the technical work that we've done because that's gonna form the basis of the next objective of the next sprint cycle. And we could come back and progressively increase the length of this sprint loop. So, the first one was done in a day, the second was in two and then four and eight and so on. But we can adjust this as is needed to determine how we could address these experiments.
But as we come back through this loop, and constantly re-rank ourselves, what was fascinating is that after only four weeks, the answer never changed again. The order of the top six wells was always the top six. And what that shows you is that really, with some quite simple approaches you can get to the same decision that you could with a full Frankenstein model. You can get to that recommendation without having to do years' worth of work.
So, we were able to make that recommendation. It was an expensive campaign, so we didn't stop at four weeks. We ended up stopping at about four months. But really importantly, we would've taken six months, perhaps a year to get that kind of a stage and we were already significantly ahead of that at the end of this routine. So, it's a method that has really changed the way I do my work, and it's something I really recommend you give a go. Hopefully, you enjoyed this, and I'll see you back here next time at the Cognitive Whiteboard.