Communications in Large Organisations: Supply Chain Management of Information

Kirsty is back with a lighthearted look at how to grease the wheels of communication and knowledge transfer in your organisation…and - yes - she does appear to have a new hair colour in this video too!

For a closer look at those personas click on the image below:

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Hello, welcome back to the Cognitive Whiteboard. My name is Kirsty. And today I'm going to talk to you about something a little bit different. I'm going to talk about communication in an organisation. Why is communication and the flow of information so important? Well, it's the lifeblood of the organism. It's the fuel that drives the machinery of any business, drives those business decisions to be made. When I think about the relationships that I have in the company and how I like to communicate with them, I divide them into personas. I find it helps me understand what people's roles are in the company. And once I've understood what their role is, it allows me to understand what I need to give them to be able to do their role. 

And here, I've just put some of them, examples of these personas, up on the board. They might be technical, or they could be more about helping you understand the way the business functions, the organisation that you're working in. But once you've identified the different personas, it makes it really easy to identify what their role is and, therefore, what you need to give them so that they can do their role. And one really good illustration of this is that as part of this subsurface workflow, we always end up having to go through the assurance stage. And it can really feel like the assurance team's job is to stop you getting where you need to go. 

But when you think about their persona, their role in the organisation, you see that what it is to do is to ensure portfolio consistency. Their job is to make sure that every opportunity development across the organization has been assessed to the same standards. And the reason that they need to be assessed at the same standards is so the decision-making team can understand that they are evaluating everything at the same level so that the top-down decision, the flow of money can happen appropriately. And once you understand that's their role, then it's really easy to see what you need to give them. You need to show them the evidence that you've applied the company's standards to your opportunity and where you haven't been able to what other solution you've used and the reasons why you've used that solution. 

So when you start to think about the personas, you start to see the company more as a machinery where every role is a different cog and the flow of information, the communication is the fuel that drives those cogs and the oil that lubricates them. And that's a much better way, I think, of visualising your company than maybe the organisational top-down organisational structures or even these more matrix-style organisational structures, because it really helps you understand what relationships you need to build. And I think that's really key. 

If you see a new executive come into a company, they'll spend quite a lot of time wandering around talking to people. And that's what they're doing. They're getting to understand how the machinery of their organisation works. They're not just coming straight in using the chain of commands to make big decisions and changes. They get to know the organisation, because everyone's individual before they make those big decisions. Well, I like to use personas to help understand my organisation, be really interested to see how you like to do it. Thanks for joining me at the Cognitive Whiteboard. And I look forward to seeing you again soon.

Dragon Eggs and Unicorn Tails

Luke introduces the new series of whiteboard videos by telling us about the myth of hard data.

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Hello and welcome back to the Cognitive Whiteboard. It's been a while but we have a new cast of characters that we will be introducing shortly. But I'm going to kick off the first of this series of videos with an attack on the hardness of the oil field data sets. To begin with, let's do some mathematics, not a place I normally start with, but if we look at a grid cell in a geological model, let's have a look at the reality of how well we've sampled that single grid cell, let alone the rest of the field.
By the time we get down the reservoir, we usually around a seven inch bit sample, doesn't really matter, but let's assume seven inches and a pretty common grid cell size might be 50 by 50 meters. If we do the mathematics on calculating the sample rate, our well bore area is about 0.02 of the square meter converted into metrics and the grid cell is around about two and a half thousand square meters of rock area, so that sample rate is 1 in 125,000. Question: does that well bore represent the perfect average of that grid cell? Let's just put it and leave it there for now.
But let's have a look at an oil field for example. Let's take Britain's biggest oil field the Forties. We have a hundred and three wells in it at 90km2 of area. Do the same mathematics and we are at 1 in 45 million as a sample right for that oil field. So even in this well-developed field, we have a pretty big challenge in trying to say we have statistics here, perhaps that's the reason why we use the term ‘geostatistics’, as to whether we want to be explicitly honoring all the mathematics to this or we want to be a little bit pragmatic and understand that our sample rates are a bit spurious. I would argue on the side of using a little bit of geological intelligence rather than just mathematics here, which is often where we start. But let's even look in a single well bore just how confident we are that we know where that well bore is. 
I was involved in a peer review where we had an issue that one of the wells was off by more than a 150m at the bottom hole location and that was proven because the velocity anomaly that was required to tie that well was just unheard of. It turned out the well was actually on the down thrown inside of a fault where it had been previously assumed to be on the up thrown side. That was discovered because we did a gyro survey over these wells to try to explain some of the issues. We found that about 30% of the wells were off by more than 50 meters and when we corrected all of those we added about 90 million barrels of oil back into that oil field and suddenly all the production history, you know, the general behavior that field started making a lot more sense.
Let's talk about that production history though. On the single well basis, how confident are we that we know the production is what we say it is? And this is probably some of the softest data that we have in the oil industry. The production data particularly when you're looking at a downhole zonal allocation can be very, very subject to uncertainty and inaccuracy. The well bore itself is often in practical terms not perfect. Cement bonds can create leakage points behind pipe, the jewelry itself wears over time, and the control of the flow can become problematic, and most of the time, wells are being produced through a cluster so the allocation back to the single well, let alone the zone can be really problematic. 
When we look at these production allocations, it's just worth bearing that in mind. Just a really hilarious point to that, we had a 28-day cycle in one oil field that turned out to be due to the hitches of the operational guys. One of the blokes was measuring the production data accurately, the other guy was just kind of eyeballing it from a distance, and that ended up with this 28-day cycle to our production data that we thought was tidal to start with. In reality, it was just inaccuracy in that measurement method. When the questioning comes, do I honor all of my all of my data? I do feel a little bit like Gandalf going up against the Balrog because the reality is I can't match all of it. Most of the time, there is going to be inaccuracy somewhere in the piece of the of the puzzle and I can't always be confident where that lies. What I'm always trying to do is develop the most coherent story I can within the realms of uncertainty that these data provide. Just a little bit of a story there, I hope that's helpful to you, if you've come across any other strangeness in your fields that turned out to be part of this, I'd love to hear about it in the comments below. That's all for now from the Cognitive Whiteboard. I'll see you back here again time.

Learning to Speak Cajun: Geomodeling for Well Planning

Luke is back at the Cognitive Whiteboard, looking at the importance of learning the language and processes of your colleagues when planning wells.

Click on the image below for a detailed view of this week's whiteboard.

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Hello, welcome back to the Cognitive Whiteboard. My name's Luke, and today I'm going to use my outrageous Australian accent to try to speak Cajun. I want to talk you through a drilling history that I've been involved with, a six-well campaign where something went wrong and we used geomodeling methodologies to make sure that it wouldn't happen again. 

So, let me talk you through the field firstly. It's a salt raft on the West African coast that has a number of different reservoir units. The main productive interval was this yellow one through here where the lower two units particularly were of much better quality and we'd had a lot combining production across those zones. The field had initially been exploited on primary decline and then water injection, so it's quite a complex reservoir management story. 

What we needed to do was make a better job of exploiting the shallowest unit of that main reservoir. We had, however, a reservoir above us that had been under production for some time, and it had some changes that had resulted in the pressure regime associated with that that we hadn't accounted for properly. So, in the pre-drill pressure prediction, we essentially said that the   pressure was going to be relatively consistent across the field. The fracture gradient would therefore be also pretty similar, and our mud weight window required only one casing depth, and we would drill through both the shallow unit and the target reservoir with the same open hole section. 

When we drilled it, however, we encountered a problem, and the problem was this. We had had, above that target, the biggest producer of that shallow reservoir, and so it had been causing a lot of pressure depletion locally in that area. We had a subsurface blowout at that point. We had a kick that we hadn't accounted for, we didn't anticipate it, it hadn't been observed before, a small kick that would've been easily contained by the well design. However, with this depletion above here, the impact of that on the gradient of the shallow reservoir with that additional kick weight resulted in a fracture inside here and massive losses of our mud system into that reservoir unit. We even ended up producing quite a lot of that drilling mud from the reservoir later on.

So, we had a situation where that well design didn't work. The problem was we had five other wells that were designed identically that needed to do the same job, and from the driller's perspective, they were not going to go near it and touch that with a bargepole. We were just post Macondo, so everyone was very sensitive around our drilling parameters. We didn't want to have anything going wrong, even more than normal, and so what they wanted to do was redesign with an additional casing string that would have required us to case between the two units that are quite close together preventing us then from being able to get a horizontal section into the target reservoir. 

That sub-vertical production would have resulted in a roundabout 60 million barrels of lost reserves. So, we really needed to make sure that that had to happen. This was a field in late production life, so it was probably the last campaign that would ever exploit that particular reservoir, and we really wanted to make sure that this is what had to be done.


We spent some time with the drillers and we realized that they do a lot of their benchmarking in 1D. They're doing that in these kinds of pressure elevation plots, so they just dump all of the wells, essentially, onto these kinds of diagrams and look out for these red spots where there's some crossover and say, "Well, there's a one in X number of chance of that happening, therefore, that's not an acceptable risk." 

What we were able to do by building a geomodel that went all the way through to the surface and incorporating all of the basic rock properties of these reservoirs but actually copying the pressure matched current day pressures from all of our simulation history models. We were able to show that the three-dimensional relationships of these pressures today under the current poor pressure regime meant that the other wells were going to be safe with that design. In fact, they're all placed underneath injectors surprisingly, and the effect was actually reversed. 

So, by putting all of this together, inverting our model into drilling mud weight, we were able to generate a example using geomodeling technologies that communicated very, very clearly to the drillers that the wells in the remaining campaign would be safe. So, we executed the plan as we had initially designed the wells, and they all came in safely, and they're now producing very nicely. So, without doing this kind of a work and linking it all together into a single story using those three-dimensional models and the current pore pressure matched simulation models, we would've missed some 60 million barrels of additional reserves. 

I hope you liked this little example. I would love to hear your stories around similar problems during a production scenario. Thank you very much.




My 24 Million Dollar Mistake

At the Cognitive Whiteboard this week Luke describes a 24 million dollar mistake that he made, and how it changed the way he thinks as a geologist.

Click on the image below to get a more detailed view of this week's Cognitive Whiteboard.



My 24 Million Dollar Mistake

Hello, welcome back to The Cognitive Whiteboard. My name's Luke and today we're not going to talk about technical best practices. I'm going to share with you an example of why I think communication is at least half the job that we do. I'm gonna illustrate that with an example from my history where I think I made a 24 million dollar mistake in an appraisal well.

Firstly, the well was drilled safely and it was drilled with no environmental impacts, and we achieved all of our appraisal objectives on time and on budget. So it wasn't a mistake in that regard - but I will explain to you why I think it is. So we had a setting where we were drilling for a lowstand sandstone. It was unusual target for the region. Typically, we were looking for something much deeper, but this lowstand was essentially within marine shales. It was in 1,500 meters of water - so quite deep for us to drill from - and it was underlying a very complex overburden of submarine canyons of cuts and fills, filled with various clay stones and calcilutites making it very, very difficult depth conversion.


Unusual Reservoir

The reservoir itself as well was quite unusual. What we had in this reservoir was a structural clay, so if you haven't seen this before, it's common for us to see dispersed clays - typically orthogenic cements that are occurring at the grain boundaries. We have laminated cements that are commonly depositional. Structural clays, though, few of us had ever encountered where essentially bioturbation had been so pervasive that these little creatures had essentially concentrated all the clay into fecal pellets, and it was providing framework support for the reservoir. So despite a 30% to 40% clay content, we had fantastic porosity and permeability.

However, those pellets were relatively ductile and what we observed in the core was that we would see a dramatic reduction in porosity and permeability associated with increasing external stresses. So the theory then was that if we went down deeper in depth, particularly below mud line, we would probably expect to see a poorer quality reservoir.

"What do you mean, you drilled it anyway?"

"What do you mean, you drilled it anyway?"

We Were Right - But We Were Wrong

And so I went to my mentor and explained that this had happened. And to my surprise, he put his head in his hands and said, "If you knew the answer, why did you drill that well?" And I really... This is a turning point in my career. It really put me back on my heels. And this is where I think my 24 million dollar mistake came. If we knew this so well and we had such good technical justification, had we worked more on our "Rosetta Stone" of translating technical jargon to business speak at this conversation, had we build a bridge between these two divides and managed that, we may have postponed a 24 million dollar drill. 

Now this was a major capital project and so it was always going to get drilled, so it is a data point that we needed, but could it have been delayed? I don't know that for sure, but I look back on that and I reference this in my career - and that's the reason why I spend so long on these boards - because communication is at least half of the job that we should be doing as a geologist.

Thanks very much. I'll see you again here next time.