Some strategies to integrate all your data, and I mean ALL your data, into better geological models that you can actually use to learn about your reservoir
Published May 2015 Here
This post is based on a couple of conference talks I gave last year (so please give much of the credit to my co-authors) focusing on building a better geological models the McMurray Formation, but there are lessons here which may be applied to just about every heterogeneous reservoir. Do you know one that isn’t heterogeneous? Me neither. Because of this I started Applied Realistic Geoscience to focus on integrating all the data we have to build something that will be proactively useful and that can meaningfully “see” below seismic resolution in many cases, rather than just something which is thrown together for simulation purposes late in the game. You can find some of the references and other information at www.realisticgeoscience.com.
There are a few things we should be aware off up front. The McMurray Formation is by general consensus a fluvial estuarine environment. As such, we see fluvial features such as point bars, and yet we see the effects of sea water in both the sedimentary features (tidal couplets) and the extent of the mud deposition. It is important to keep in mind what these systems actually look like when we are building a detailed and useful model. This has ramifications for everything from cell size to stratigraphic architecture, so it is important to give it its full consideration in order to get it right, right at the beginning. Check out figure 1. This is a video taken from Google Earth Engine which is an animated globe incorporating almost 30 years of satellite imagery that Google has acquired. This is a fantastic dataset, and very useful for getting an idea of what the real world looks like, and how it changes over a human timescale. Figure 1 is focused about 300 km away from Lima in Peru, and really gives a great view of the amount of change you can see in a relatively short amount of time in a fluvial system. Take a second and watch it go around a few times, look for how the river erodes and causes truncations. Look also at the shapes you see, and what a point bar actually looks like in plan view, in “the wild”. These are the kind of shapes we should be using to build our models, and if we aren’t, then were not doing a good enough job. We know they are there, we can see them on seismic in many cases (see figure 2), why don’t we incorporate them?
Figure 1The El Sira River ~300km from Lima, Peru
Figure 2 from LABRECQUE et al.2011: point bars revealed in seismic of the McMurray
So now we know what point bars look like in plan view, how about in cross section? In a great analogue from Dinosaur Provincial Park (figure 3), we see the kind of structures we should be seeing in the subsurface of the McMurray Formation. We have inclined fluvial strata deposited via lateral accretion as a point bar develops, and an abandonment facies where the channel gets filled in by mud when the point bar gets cut off by the river finding a new course.
This is great as far as it goes, but the scale of the sediments preserved at Dinosaur Provincial Park is much smaller than those preserved in the McMurray Formation, so it’s time to go to the horse’s mouth, and actually have a look at it at the mine face (Figure 4). Note the inclined nature and variable mud lengths within the bedding. The geometries, sedimentology and stratigraphy we see are important, and are features that should be incorporated into our model.
See how far we have come already? We know how the model should look in both plan view and cross section, and we haven’t even looked at any specific core, petrophysical or seismic data from the McMurray yet. The situation is improving, but you do still see McMurray models which are essentially statistical fuzz, and do not even attempt to incorporate the features that can be observed in the figures we have considered thus far.
Let’s take a look at the data we can glean from wells, and see how we can incorporate this meaningfully into our model. You can get a lot out of well data, it always drives me crazy when an operator on a misguided mission to cut costs decides to cut back on the core and wireline log data collected. These days, the actual cost of the coring and logging is so low compared to the overall drilling costs that is makes very little sense to cheap out on the data. After all, the mission isn’t simply to drill a successful hole (despite what some rig managers seem to think), it is to collect as much data as possible from the subsurface. For me this includes core, FMI (Formation Micro Imager), sonic, and at least a triple combo.
If you don’t collect core, you don’t know your oil saturation (yea yea, Archie etc. but I have definitely seen some strange resistivity responses that would make you think you had drilled a duster, when upon examining the core, you actually have 20m of beautiful oil charged sand), you have very limited information on your particle size distributions, you have no ability to evaluate permeability, you don’t have any information on any small mud beds that are too thin to be resolved on your well logs yet effect your permeability……. I could go on.
FMI gives you a high resolution, wrap around image of the wellbore, and therefore the rock that has been drilled through. This is useful not just because you can look at the image and identify sedimentary features and surfaces, but also because analysis of this image gives you precise information regarding the orientation of the features observed. This has proven useful for understanding the stratigraphic architecture of your reservoir (by identifying the orientation of bedding etc.), and gives you information on your stress regime (by identifying the orientation of fractures). I have found it invaluable to identifying sand on sand contacts by seeing a dramatic change in dip orientation in the middle of a sand body, and identifying an incised channel in an otherwise sandy reservoir in the same way. Important observations that would otherwise be missed. In a pinch, a regular, non FMI dipmeter can useful, but as this uses fewer sensors, it doesn’t give you the image of the FMI, and doesn’t allow the direct discrimination between dunes, bedding and fractures, amongst other things. More on this later.
If you don’t collect sonic data, you can’t tie your wells to seismic. Which means you don’t know for sure where you are in depth on your seismic, which is pretty important. Imagine if you are making drilling decisions based on an interpreted seismic survey, only to find that you were looking in a shallower or deeper zone than your target.
Without a triple combo (which is a gamma ray tool, porosity tool and resistivity tool) you seriously limit your ability to build a stratigraphic framework and correlate between wells, in addition to losing out on the other benefits of understanding porosity, resistivity and gamma ray signals.
But I digress. Knowing what we do about the general style of the sedimentary environment, we can inform this conceptual model with data from the wells. We can build a stratigraphic framework based on the correlations we make between wells (based on all the data of course) and gain an understanding of the vertical trends in porosity, grain size, oil saturation….. i.e. everything that is important to your method of production. You might be surprised by how consistent individual stratigraphic units can be, if you can identify individual bars etc. This kind of information will prove invaluable when it comes time to populate your model with the statistics describing your data. For now, the key item is to identify the main surfaces so we can begin to make meaningful seismic interpretations, and provide accurate stratigraphic surfaces to build the structure of the model.
But well data, however powerful, is only giving you a 2 dimensional view of the subsurface. It becomes more powerful when combined with seismic data, and we can meaningfully join our stratigraphic framework picks, made using well data, in three dimensions. It’s like a 3d “join the dots” puzzle, and can be great fun. This might also be where you go back and re-evaluate your well data. If based on the core, you thought you were dealing with an incised valley system, but the seismic looks like a delta, you need to think again about what these data sets are telling you. For this reason, you might end up iterating between the well data, core and seismic to hone your interpretations.
I’ll be honest, I am not a geophysicist, but I will trust the insights of the skilled professionals I have worked with that you can correlate various aspects of the seismic data with porosity, density, and perhaps even oil saturation. I have however interpreted a lot of 3D data (known in the biz as cubes, even though the surveys are never cubic), and the various other seismically derived volumes just mentioned. I just need them depth converted etc. first. Being a geologist is advantageous when it comes to interpreting seismic, simply because geologists tend to have a better mental image of what the reservoir looks like. I just want to emphasize that integrating seismic should be a team effort, with every team member bringing what they do best to the forefront.
However it is also important to know the weaknesses of seismic. I’m speaking specifically about resolution. The lateral resolution varies, but is commonly on the order of 10m, and the vertical resolution is perhaps 8m, and may vary with depth. I won’t get into the technical details of tuning thickness, fold etc. but, while every survey is different, the resolution is often insufficient to identify individual beds in a complicated reservoir. It is an underappreciated aspect of a good model that you can combine all your data to essentially “see” below seismic resolution. You can see from figure 2 that good quality seismic imaging can tell you a lot about the reservoir, but the collection of seismic data is effected by many factors such as surface condition (if your survey was collected on land), charge size, geophone density, reservoir thickness and reservoir composition (amongst much else). To see the kind of detail revealed in figure 2 you often need an expensive seismic shoot and a little gas in the top of the reservoir. The density contrast really lights the data up. However, this density contrast often disappears when you go below the gas zone, so it gets difficult to see the detail of the reservoir below this resolution. In addition, the non-optimal seismic imaging of a reservoir containing virtually no gas can look like figure 5, which is useful of correlating major stratigraphic surfaces, but it is a major challenge to get much else out of it.
However, all is not lost, we still have the surfaces to build the structure of our model, and we can still use what we have to build a realistic model of the subsurface. We just have to work a little harder. So use the surfaces you have interpreted to build your structural framework, and let’s move on to figuring out how to build the layering scheme between these surfaces.
Let’s now consider the FMI/dipmeter for a moment. As previously discussed, it can provide valuable information on the orientation of the bedding in the subsurface. How can we use this to build a better model? Consider Figure 6, and pay close attention to the two rightmost tracks.
The second from the right track is commonly called a tadpole plot, and this is the starting point for examining dipmeter data. The direction of the point on the dots indicates the dip direction (relative to north) and the lateral position indicates the severity of the dip. In this way it is possible to identify dunes, IHS (inclined heterolithic stratification) and major changes in dip direction may indicate a major stratigraphic surface which might be difficult to identify otherwise. The well in Figure 6 was selected because it represented a fairly simply, unidirectional case, but even so, the high magnitude dips at the base of the sandy section represent the dune system that was migrating through the thalweg of the channel, and the lower magnitude dips represent the IHS deposited via lateral accretion above this, with a reactivation part way up the section. The right most track on figure 6 is a rose diagram displaying all the data from the dipmeter track over a given interval. It is clear here that the sandy fluvial section of this well has a very strong northeast orientation, with an average dip of ~7 degrees. This is useful information on the orientation of these beds at this location. Map this dominant dip direction spatially as in figure 7, and you can see how we can build a surface that honours this data, and can provide a layering scheme for part of the model.
With a little know how, you can build a surface using this kind of data which honours not only the dip direction, but also the average dip magnitude as in Figure 8. This is what you would use to build your layering scheme.
But say you don’t have a lot or perhaps any dipmeter data? Here is another strategy you can employ to get similar results. In this example, we have one well on which someone ran a dipmeter, but we also have some horizontal well data which we can use to establish a layering surface. Consider figure 9 which contains a log of a different well, but laid out in a similar fashion to figure 6.
Note that like figure 6 this well has a very strong orientation in the sand prone section. However, without other dipmeter wells nearby, what can we use to build a reliable layering surface? In this instance, we have some additional MWD (measure while drilling) data from 10 horizontal well pairs which proved useful. Consider figure 10.
Here we see the deep resistivity profiles from the upper wells on the pad along with the dipmeter well near the centre of the image. The colour scheme and thickness of the resistivity data have been manipulated to show the subtle trend of a bed which intersected 6 of the wells in a smooth arc. The magnitude of dip observed in figure 9 lines up beautifully with this trend and allows the construction of a layering surface which honours all of this data, which is shown in figure 11.
Once we have established a dipping surface, we can use it to guide the layering in the model. The next step is to consider the intuition behind some of the techniques which are commonly used to build the geostatistical part of a model.
I won’t go into using data analysis to figure out what vertical and lateral trends you have in your reservoir because it is going to be different for every model you build. Suffice to say that you need to honour any trends you identify from your interrogation of the data you have available. Know that your grain size becomes finer as you move up vertically? Then you need to build that trend into your geostatistics. What does this change in grain size imply for your permeability? Perhaps we should be honouring that as well.
However, the first model you need to build is a facies model. By this point you should have identified a number of lithofacies, electrofacies or some other kind of facies from your log and core data which you think accurately represents the rock types you observe there. This is often done based on mud content. 0-5% mud being facies 1, 5-15% facies 2 etc, which is fine and easy as you can often just load the core description provided by the core handling company or the geologists hired to log the core. A better way would be to identify lithofacies associations based on sedimentalogical processes, for reasons that will become clear shortly. Nardin et al. 2013 provide an excellent scheme for the McMurray Formaton, and this is very close to the scheme that is used in the example to come.
An important step in building your facies model is considering the size of the variogram you want to use. Variograms are often poorly described and misunderstood, but an easy way to visualise what they are is to think about a rugby ball around one of your data points in space. The larger this rugby ball, the further the algorithm will look to try and join up two points of the same value. In effect, the larger your variogram, the larger your facies will be in three dimensions. But this tells us nothing about how big should make them. There are variogram analysis tools available to analyse the lateral and vertical variability of your data set, but I suspect they are not going to be a lot of practical use, except for analysing the vertical, particularly in the McMurray (or any other fluvial system), and here is why: Facies in the McMurray are generally too small to be intersected by more than one well. Consider figure 12
This is another annotated McMurray lidar image from Findlay et al 2014 with two hypothetical wells drawn on for emphasis. We know that the McMurray contains dipping beds, and we know that they dip at 5-10 degrees. Now assume you cut that so that apparent dip is the same as actual dip, and that you have a 35m thick reservoir which is dipping at 5 degrees. The geometry of this means that if you want to see the same bed twice, you need to have a well every 400m. Do you have that kind of well density? Not many do. Things get worse though if you have steeper dips, you will need finer spaced wells. Also, even if you have 400m well density, you have other problems. How do you know which bed in well b is the same as the bed you are looking at in well a? You don’t, and even if you did, you would have only 2 data points for that plane. Will it have the same facies in both wells? Unlikely, but even if it did, is 2 data points adequate to assess the variability along that plain within the reservoir? Almost certainly not.
I took you a long way to make the small point that you cannot use well based variogram analysis to define your lateral facies variogram dimensions, unless your facies lateral extent is considerable larger than your well spacing. This is certainly not the case in the McMurray.
So if we can’t use the variogram tools to define our lateral variogram dimensions, what should we do? I suggest we use actual measurements from the formation in question. Consider figure 13, modified from Nardin et al, 2013.
Nardin et al (2013) painstakingly built a facies scheme, and measured the length of these facies in the McMurray Formation along a mine face, often in dip and strike direction (amongst a lot else, check the paper out. It’s a goodie). In this figure we can see that the mode of lithofacies association c in the strike direction is about 5m and in the dip direction about 8. This means that the most common bed of this lithofacies association would be 5m wide, and 8m long, orientated down the dipping bed. Let’s think for a minute what that means for our model. What cell size did we use? Did we leave it on the default 50m x 50m square? Better rethink that. If we don’t use a small enough cell size, we wont see any of this rock type in the model. There are essentially no exposures of this lithofacies association that are 50m x 50m in lateral extent. It also means that we can perhaps extract from this data what our variogram dimensions should be (i.e. 5mx8m). We can do something similar for the rest of the lithofacies we are going to use, and specify our variogram dimensions accordingly.
Now we have all the pieces in place, we can build our facies model. The kind of methodologies described here built the model exhibited in figure 14. The model isn’t supposed to look exactly like the Lidar image, they are hundreds of kilometres apart in reality, but I think you will agree that the stratigraphic architectural style has been preserved. This fine detail matters when we need to simulate as subtle permeability changes can have a huge effect on steam propagation, and we can be confident that our model is seeing below seismic resolution (compare figure 14 with figure 5, see what I mean?). With this facies model built, we can start to populate other properties in a realistic manner, but that is a post for another day.
Thanks for reading this post, we hope you have found it insightful. You can email me at firstname.lastname@example.org, visit www.realisticgeoscience.com , or follow the company on LinkedIn and Twitter. Perhaps Realistic can be of use to you? Look forward to hearing from you.
Findlay, DJ, Nardin, T, Couch, A. and Wright, A , 2014 Modeling Lateral Accretion in the McMurray Formation at Grizzly Oil Sands Algar Lake SAGD Project, Canadian Heavy Oil Conference, Calgary, November 2014.
Findlay, DJ, Nardin, T, Wright, A and Mojarad, RS, 2014, Modeling Lateral Accretion in McMurray Formation Fluvial-Estuarine Channel Systems: Grizzly Oil Sands’ May River SAGD Project, Athabasca, Geoconvention 2014, Calgary, May 2014.
Labreque, PA, Hubbard, SM, Jenson, JL, and Nielson H, 2011, Sedimentology and stratigraphic architecture of a point bar deposit, Lower Cretaceous McMurray Formation, Alberta, Canada, Bulletin of Canadian Petroleum Geology 59, No. 2, p. 147–171.
Nardin, TR, Feldman, HR, and Carter, BJ, 2013, Stratigraphic Architecture of a Large-Scale Point Bar Complex in the McMurray Formation: Syncrude’s Mildred Lake Mine, Alberta, Canada. in FJ Hein et al (Eds.). Heavy-oil and Oil-sand Petroleum Systems in Alberta and Beyond. AAPG Studies in Geology 64, p. 273-311.