The Rock Property Catalog again

Do you like data? Data about rocks? Open, accessible data that you can use for any purpose without asking? Read on.

After writing about anisotropy back in February, and then experimenting with storing rock properties in SubSurfWiki later that month, a few things happened:

• The server I run the wiki on — legacy Amazon AWS infrastructure — crashed, and my backup strategy turned out to be <cough> flawed. It's now running on state-of-the-art Amazon servers. So my earlier efforts were mostly wiped out... Leaving the road clear for a new experiment!
• I came across an amazing resource called Mudrock Anisotropy, or — more appealingly — Mr Anisotropy. Compiled by Steve Horne, it contains over 1000 records of rocks, gathered from the literature. It is also public domain and carries only a disclaimer. But it's a spreadsheet, and emailing a spreadsheet around is not sustainable.
• The Common Ground database that was built by John A. Scales, Hans Ecke and Mike Batzle at Colorado School of Mines in the late 1990s, is now defunct and has been officially discontinued, as of about two weeks ago. It contains over 4000 records, and is public domain. The trouble is, you have to restore a SQLite database to use it.

All this was pointing towards a new experiment. I give you: the Rock Property Catalog again! This time it contains not 66 rocks, but 5095 rocks. Most of them have $$V_\mathrm{P}$$, $$V_\mathrm{S}$$ and  $$\rho$$. Many of them have Thomsen's parameters too. Most have a lithology, and they all have a reference. Looking for Cretaceous shales in North America to use as analogs on your crossplots? There's a rock for that.

As before, you can query the catalog in various ways, either via the wiki or via the web API. Let's say we want to find shales with a velocity over 5000 m/s. You have a few options:

1. Go to the semantic search form on the wiki and type [[lithology::shale]][[vp::>5000]]
2. Make a so-called inline query on your own wiki page (you need an account for this).
3. Make a query via the web API with a rather long URL: http://www.subsurfwiki.org/api.php?action=ask&query=[[RPC:%2B]][[lithology::shale]][[Vp::>5000]]|%3FVp|%3FVs|%3FRho&format=jsonfm

I updated the Jupyter Notebook I published last time with a new query. It's pretty hacky. I'll work on this to produce a more robust method, with some error handling and cleaner code — stay tuned.

The database supports lots of properties, including:

• Citation and reference
• Description, lithology, colour (you can have pictures if you want!)
• Location, lat/lon, basin, age, depth
• Vp, Vs, $$\rho$$, as well as $$\rho_\mathrm{dry}$$ and $$\rho_\mathrm{grain}$$
• Thomsen's $$\epsilon$$, $$\delta$$, and $$\gamma$$
• Static and dynamic Young's modulus and Poisson ratio
• Confining pressure, pore pressure, effective stress, axial stress
• Frequency
• Fluid, saturation type, saturation
• Porosity, permeability, temperature
• Composition

There is more from the Common Ground data to add, especially photographs. But for now, I'd love some feedback: is this the right set of properties? Do we need more? I want this to be useful — what kind of data and metadata would you like to see?

I'll end with the usual appeal — I'm open to any kind of suggestions or help with this. Perhaps you can contribute new rocks, or a paper containing data? Or maybe you have some wiki skills, or can help write bots to improve the data? What can you bring?

Matt Hall

Matt is a geoscientist in Nova Scotia, Canada. Founder of Agile Scientific, co-founder of The HUB South Shore. Matt is into geology, geophysics, and machine learning.

Which brittleness index?

A few weeks ago I looked at the concept — or concepts — of brittleness. There turned out to be lots of ways of looking at it. We decided to call it a rock behaviour rather than a property. And we determined to look more closely at some different ways to define it. Here they are...

Some brittleness indices

There are lots of 'definitions' of brittleness in the literature. Several of them capture the relationship between compressive and tensile strength, σC and σT respectively. This is potentially useful, because we measure uniaxial compressive strength in the standard triaxial rig tests that have become routine in shale studies... but we don't usually find the tensile strength, because it's much harder to measure. This is unfortunate, because hydraulic fracturing is initially a tensile failure (though reactivation and other failure modes do occur — see Williams-Stroud et al. 2012).

Altindag (2003) gave the following three examples of different brittleness indices. In turn, they are the strength ratio, a sort of relative strength contrast, and the mean strength (his favourite):

This is just the start, once you start digging, you'll find lots of others. Like Hucka & Das's (1974) round-up I wrote about last time, one thing they have in common is that they capture some characteristic of rock failure. That is, they do not rely on implicit rock properties.

Another point to note. Bažant & Kazemi (1990) gave a way to de-scale empirical brittleness measures to account for sample size — not surprisingly, this sort of 'real world adjustment' starts to make things quite complicated. Not so linear after all.

What not to do

The prevailing view among many interpreters is that brittleness is proportional to Young's modulus and/or Poisson's ratio, and/or a linear combination of these. We've reported a couple of times on what Lev Vernik (Marathon) thinks of the prevailing view: we need to question our assumptions about isotropy and linear strain, and computing shale brittleness from elastic properties is not physically meaningful. For one thing, you'll note that elastic moduli don't have anything to do with rock failure.

The Young–Poisson brittleness myth started with Rickman et al. 2008, SPE 115258, who presented a rather ugly representation of a linear relationship (I gather this is how petrophysicists like to write equations). You can see the tightness of the relationship for yourself in the data.

If I understand  the notation, this is the same as writing B = 7.14E – 200ν + 72.9, where E is (static) Young's modulus and ν is (static) Poisson's ratio. It's an empirical relationship, based on the data shown, and is perhaps useful in the Barnett (or wherever the data are from, we aren't told). But, as with any kind of inversion, the onus is on you to check the quality of the calibration in your rocks.

What's left?

Here's Altindag (2003) again:

Brittleness, defined differently from author to author, is an important mechanical property of rocks, but there is no universally accepted brittleness concept or measurement method...

This leaves us free to worry less about brittleness, whatever it is, and focus on things we really care about, like organic matter content or frackability (not unrelated). The thing is to collect good data, examine it carefully with proper tools (Spotfire, Tableau, R, Python...) and find relationships you can use, and prove, in your rocks.

References

Altindag, R (2003). Correlation of specific energy with rock brittleness concepts on rock cutting. The Journal of The South African Institute of Mining and Metallurgy. April 2003, p 163ff. Available online.

Hucka V, B Das (1974). Brittleness determination of rocks by different methods. Int J Rock Mech Min Sci Geomech Abstr 10 (11), 389–92. DOI:10.1016/0148-9062(74)91109-7.

Rickman, R, M Mullen, E Petre, B Grieser, and D Kundert (2008). A practical use of shale petrophysics for stimulation design optimization: all shale plays are not clones of the Barnett Shale. SPE 115258, DOI: 10.2118/115258-MS.

Williams-Stroud, S, W Barker, and K Smith (2012). Induced hydraulic fractures or reactivated natural fractures? Modeling the response of natural fracture networks to stimulation treatments. American Rock Mechanics Association 12–667. Available online.

Comment

Matt Hall

Matt is a geoscientist in Nova Scotia, Canada. Founder of Agile Scientific, co-founder of The HUB South Shore. Matt is into geology, geophysics, and machine learning.

More 1IWRP highlights

As I reported on Wednesday, I've been at 1IWRP, a workshop on rock physics in the petroleum industry. Topics ranged from lab core studies to 3D digital scanners, and from seismic attenuation and dispersion to shales and anisotropy. Rock physics truly crosses a lot of subject areas.

Here are a few of the many great talks that really stood out for me:

Mark Chapman from the University of Edinburgh, submitted a new formulation for frequency dependant AVO analysis. He suggested that if a proper rock physics model of the rock is described, frequency can be decomposed from seismic gathers for improved reservoir characterization. Some folks in the crowd warned that the utility of this work might be limited to select cases with a full band impedance change, but his method appears to be a step beyond the traditional AVO workflow.

Arthur Cheng from Halliburton talked about modeling techniques to estimate anisotropic parameters from borehole measurements. He descibed the state of the art in acoustic logging tools, and used a ray-tracing VSP forward model to show a significant smear of reflection points through an anisotropic earth layer. He touched on the importance of close interaction between service companies and end users, especially those working in complex environments. In particular: service companies have a good understanding of data precision and accuracy, but it's usually not adequately transfered to the interpreter.

Colin Sayers from Schlumberger presented several talks, but I really enjoyed what he had to say about sonic and seismic anisotropy and how it is relevant to characterizing shale gas reservoirs. Fracture propagation depends on the 3D stress state in the rock: hard to capture with a 1D earth model. He showed an example of how hydraulic fracture behaviour could be more accurately predicted by incorporating anisotropic stress dependant elastic properties. I hope this insight permeates throughout the engineering community.

Rob Lander from Geocosm showed some fresh-out-of-the-oven simulations of coupled diagenesis and rock physics models for predicting reservoir properties away from wells. His company's workflow has a basis in petrography, integrating cathodluminescence microscopy and diagenetic modeling. Really inspiring and integrated stuff. I submit to you that this presentation would be equally enjoyed at a meeting of AAPG, SPE, SPWLA, SEG, or SCA — that's not something that you can say about every talk.

Every break heralded a new discussion. The delegates were very actively engaged.

Today, I am going on a field trip to the Niobrara Shale Quarry. After four days indoors, I'm looking forward to getting outside and hammering some rocks!

Shattering shale

In shale gas exploration, one of the most slippery attributes we are interested in is fracability. The problem is that the rocks we study have different compositions and burial histories, so it's hard to pin down the relative roles of intrinsic rock properties and extrinsic stress states. Glass could be considered an end member for brittleness, and it has fairly uniform elastic parameters and bulk composition (it's amorphous silica). Perhaps we can learn something about the role of stresses by looking more closely at how glass fractures.

The mechanics of glass can be characterized by two aspects: how it's made, and how it breaks.

Annealed glass is made by pouring molten glass onto a thin sheet of tin. Upon contact, the tin melts allowing for two perfectly smooth and parallel surfaces. The glass is cooled slowly so that stress irregularities dissipate evenly throughout, reducing local weak points. This is ordinary glass, as you might find in a mirror.

Tempered glass is made by heating annealed glass to near its softening point, about 720˚C, and then quickly cooling it by quenching with air jets. The exterior surface shrinks, freezing it into compression, while the soft interior of the glass gets pulled out by tensional forces as it freezes (diagram).

How glass is made is directly linked to how it breaks. Annealed glass is weaker, and breaks into sparse splinters. The surface of tempered glass is stronger, and when it breaks, it breaks catastrophically; the interior tensional energy releases cracks from the inside out.

A piece of tempered glass is 4-6 times stronger than a piece of annealed glass with the same elastic properties, composition, density and dimensions. This means it looks almost identical but requires much more stress to break. Visually and empirically, it is not easy to tell the difference between annealed and tempered glass. But when you break it, the difference is obvious. So here, for two very brittle materials, with all else being equal, the stress state plays the dominant role in determining the mode of failure.

Because natural permeability is so low in fine grained rocks, production companies induce artificial fractures to connect flow pathways to the wellbore. The more surface area exposed, the more methane will be liberated.

If we are trying to fracture-stimulate shale to get at the molecules trapped inside, we would clearly prefer shale that shatters like tempered glass. The big question is: how do we explore for shale like this?

One approach is to isolate parameters such as natural fractures, anisotropy, pore pressure, composition, and organic content and study their independent effects. In upcoming posts, we'll explore the tools and techniques for measuring these parameters across scale space for characterizing fracability.