Bring it into time

A student competing in the AAPG's Imperial Barrel Award recently asked me how to take seismic data, and “bring it into depth”. How I read this was, “how do I take something that is outside my comfort zone, and make it fit with what is familiar?” Geologists fear the time domain. Geology is in depth, logs are in depth, drill pipe is in depth. Heck, even X and Y are in depth. Seismic data relates to none of those things; useless right? 

It is excusable for the under-initiated, but this concept of “bringing [time domain data] into depth” is an informal fallacy. Experienced geophysicists understand this because depth conversion, in all of its forms and derivatives, is a process that introduces a number of known unknowns. It is easier for others to be dismissive, or ignore these nuances. So early-onset discomfort with the travel-time domain ensues. It is easier to stick to a domain that doesn’t cause such mental backflips; a kind of temporal spatial comfort zone. 

Linear in time

However, the unconverted should find comfort in one property where the time domain is advantageous; it is linear. In contrast, many drillers and wireline engineers are quick to point that measured depth is not nessecarily linear. Perhaps time is an even more robust, more linear domain of measurement (if there is such a concept). And, as a convenient result, a world of possibilities emerge out of time-linearity: time-series analysis, digital signal processing, and computational mathematics. Repeatable and mechanical operations on data.

Boot camp in time

The depth domain isn’t exactly omnipotent. A colleague, who started her career as a wireline-engineer at Schlumberger, explained to me that her new-graduate training involved painfully long recitations and lecturing on the intricacies of depth. What is measured depth? What is true vertical depth? What is drill-pipe stretch? What is wireline stretch? And so on. Absolute depth is important, but even with seemingly rigid sections of solid steel drill pipe, it is still elusive. And if any measurement requires a correction, that measurement has error. So even working in the depth domain data has its peculiarities.

Few of us ever get the privilege of such rigorous training in the spread of depth measurements. Sitting on the back of the rhetorical wireline truck, watching the coax-cable unpeel into the wellhead. Few of us have lifted a 300 pound logging tool, to feel the force that it would impart on kilometres of cable. We are the recipients of measurements. Either it is a text file, or an image. It is what it is, and who are we to change it? What would an equvialent boot camp for travel-time look like? Is there one?

In the filtered earth, even the depth domain is plastic. Travel-time is the only absolute.

News of the week

Our regularly irregular news column returns! If you come across geoscience–tech tidbits, please drop us a line. 

A new wiki for geophysics

If you know Agile*, you know we like wikis, so this is big news. Very quietly, the SEG recently launched a new wiki, seeded with thousands of pages of content from Bob Sheriff's famous Encyclopedic Dictionary of Applied Geophysics. So far, it is not publicly editable, but the society is seeking contributors and editors, so if you're keen, get involved. 

On the subject of wikis, others are on the horizon: SPE and AAPG also have plans. Indeed members of SEG and AAPG were invited to take a survey on 'joint activities' this week. There's a clear opportunity for unity here — which was the original reason for starting our own subsurfwiki.org. The good news is that these systems are fully compatible, so whatever we build separately today can easily be integrated tomorrow. 

The DISC is coming

The SEG's Distinguished Instructor Short Course is in its 15th year and kicks off in 10 days in Brisbane. People rave about these courses, though I admit I felt like I'd been beaten about the head with the wave equation for seven hours after one of them (see if you can guess which one!). This year, the great Chris Liner (University of Houston prof and ex-editor of Geophysics) goes on the road with Elements of Seismic Dispersion: A somewhat practical guide to frequency-dependent phenomena. I'm desperate to attend, as frequency is one of my favourite subjects. You can view the latest schedule on Chris's awesome blog about geophysics, which you should bookmark immediately.

Broadband bionic eyes

Finally, a quirky story about human perception and bandwidth, both subjects close to Agile's core. Ex-US Air Force officer Alek Komar, suffering from a particularly deleterious cataract, had a $23k operation to replace the lens in one eye with a synthetic lens. One side-effect, apart from greater acuity of vision: he can now see into the ultraviolet.

If only it was that easy to get more high frequencies out of seismic data; the near-surface 'cataract' is not as easily excised.

This regular news feature is for information only. We aren't connected with any of these organizations, and don't necessarily endorse their products or services. 

More than a blueprint

"This company used to function just fine without any modeling."

My brother, an architect, paraphrased his supervisor this way one day; perhaps you have heard something similar. "But the construction industry is shifting," he noted. "Now, my boss needs to see things in 3D in order to understand. Which is why we have so many last minute changes in our projects. 'I had no idea that ceiling was so low, that high, that color, had so many lights,' and so on."

The geological modeling process is often an investment with the same goal. I am convinced that many are seduced by the appeal of an elegantly crafted digital design, the wow factor of 3D visualization. Seeing is believing, but in the case of the subsurface, seeing can be misleading.

Not your child's sandbox! Photo: R Weller.

Building a geological model is fundamentally different than building a blueprint, or at least it should be. First of all, a geomodel will never be as accurate as a blueprint, even after the last well has been drilled. The geomodel is more akin to the apparatus of an experiment; literally the sandbox and the sand. The real lure of a geomodel is to explore and evaluate uncertainty. I am ambivalent about compelling visualizations that drop out of geomodels, they partially stand in the way of this high potential. Perhaps they are too convincing.

I reckon most managers, drillers, completions folks, and many geoscientists are really only interested in a better blueprint. If that is the case, they are essentially behaving only as designers. That mindset drives a conflict any time the geomodel fails to predict future observations. A blueprint does not have space for uncertainty, it's not defined that way. A model, however, should have uncertainty and simplifying assumptions built right in.

Why are the narrow geological assumptions of the designer so widely accepted and in particular, so enthusiastically embraced by the industry? The neglect of science keeping up with technology is one factor. Our preference for simple and quickly understood explanations is another. Geology, in its wondrous complexity, does not conform to such easy reductions.

Despite popular belief, this is not a blueprint.We gravitate towards a single solution precisely because we are scared of the unknown. Treating uncertainty is more difficult that omitting it, and a range of solutions is somehow less marketable than precision (accuracy and precision are not the same thing). It is easier because if you have a blueprint, rigid, with tight constraints, you have relieved yourself from asking what if?

• What if the fault throw was 20 m instead of 10 m?
• What if the reservoir was oil instead of water?
• What if the pore pressure increases downdip?

The geomodelling process should be undertaken for the promise of invoking questions. Subsurface geoscience is riddled with inherent uncertainties, uncertainties that we aren't even aware of. Maybe our software should have a steel-blue background turned on as default, instead of the traditional black, white, or gray. It might be a subconscious reminder that unless you are capturing uncertainty and iterating, you are only designing a blueprint.

If you have been involved with building a geologic model, was it a one-time rigid design, or an experimental sandbox of iteration?

The photograph of the extensional sandbox experiment is used with permission from Roger Weller of Cochise College. Image of geocellular model from the MATLAB Reservoir Simulation Toolbox (MRST) from SINTEF applied mathematics, which has been recently released under the terms of the GNU General public license! The blueprint is © nadla and licensed from iStock. None of these images are subject to Agile's license terms.

Open up

After a short trip to Houston, today I am heading to London, Ontario, for a visit with Professor Burns Cheadle at the University of Western Ontario. I’m stoked about the trip. On Saturday I’m running my still-developing course on writing for geoscientists, and tomorrow I’m giving the latest iteration of my talk on openness in geoscience. I’ll post a version of it here once I get some notes into the slides. What follows is based on the abstract I gave Burns.

A recent survey by APEGBC's Innovation magazine revealed that geoscience is not among the most highly respected professions. Only 20% of people surveyed had a ‘great deal of respect’ for geologists and geophysicists, compared to 30% for engineers, and 40% for teachers. This is far from a crisis, but as our profession struggles to meet energy demands, predict natural disasters, and understand environmental change, we must ask, How can we earn more trust? Perhaps more openness can help. I’m pretty sure it can’t hurt.

Many people first hear about ‘open’ in connection with software, but open software is just one point on the open compass. And even though open software is free, and can spread very easily in principle, awareness is a problem—open source marketing budgets are usually small. Open source widgets are great, but far more powerful are platforms and frameworks, because these allow geoscientists to focus on science, not software, and collaborate. Emerging open frameworks include OpendTect and GeoCraft for seismic interpretation, and SeaSeis and BotoSeis for seismic processing.

If open software is important for real science, then open data are equally vital because they promote reproducibility. Compared to the life sciences, where datasets like the Human Genome Project and Visible Human abound, the geosciences lag. In some cases, the pieces exist already in components like government well data, the Open Seismic Repository, and SEG’s list of open datasets, but they are not integrated or easy to find. In other cases, the data exist but are obscure and lack a simple portal. Some important plays, of global political and social as well as scientific interest, have little or no representation: industry should release integrated datasets from the Athabasca oil sands and a major shale gas play as soon as possible.

Open workflows are another point, because they allow us to accelerate learning, iteration, and failure, and thus advance more quickly. We can share easily but slowly and inefficiently by publishing, or attending meetings, but we can also write blogs, contribute to wikis, tweet, and exploit the power of the internet as a dynamic, multi-dimensional network, not just another publishing and consumption medium. Online readers respond, get engaged, and become creators, completing the feedback loop. The irony is that, in most organizations, it’s easier to share with the general public, and thus competitors, than it is to share with colleagues.

The fourth point of the compass is in our attitude. An open mindset recognizes our true competitive strengths, which typically are not our software, our data, or our workflows. Inevitably there are things we cannot share, but there’s far more that we can. Industry has already started with low-risk topics for which sharing may be to our common advantage—for example safety, or the environment. The question is, can we broaden the scope, especially to the subsurface, and make openness the default, always asking, is there any reason why I shouldn’t share this?

In learning to embrace openness, it’s important to avoid some common misconceptions. For example, open does not necessarily mean free-as-in-beer. It does not require relinquishing ownership or rights, and it is certainly not the same as public domain. We must also educate ourselves so that we understand the consequences of subtle and innocuous-seeming clauses in licences, for example those pertaining to non-commerciality. If we can be as adept in this new language as many of us are today in intellectual property law, say, then I believe we can accelerate innovation in energy and build trust among our public stakeholders.

So what are you waiting for? Open up!

Stop waiting for permission to knock someone's socks off

When I had a normal job, this was the time of year when we set our goals for the coming months. Actually, we sometimes didn't do it till March. Then we'd have the end-of-year review in October... Anyway, when I thought of this, it made me think about my own goals for the year, for Agile, and my career (if you can call it that). Here's my list:

1. Knock someone's socks off.

That's it. That's my goal. I know it's completely stupid. It's not SMART: specific, measurable, attainable, realistic, or timely. I don't believe in SMART. For a start, it's obviously a backronym. That's why there's attainable and realistic in there—what's the difference? They're equally depressing and uninspiring. Measurable, attainable goals are easy, and I'm going to do them anyway: it's called work. It's the corporate equivalent of saying my goals for the day are waking up, getting out of bed, having a shower, making a list of attainable goals... Maybe those are goals if you're in rehab, but if you're a person with a job or a family they're just part of being a person.

I don't mean we should not make plans and share lists of tasks to help get stuff done. It's important to have everyone working at least occasionally in concert. In my experience people tend to do this anyway, but there's no harm in writing them down for everyone to see. Managers can handle this, and everyone should read them.

Why do these goals seem so dry? You love geoscience or engineering or whatever you do. That's a given. (If you don't, for goodness's sake save yourself.) But people keep making you do boring stuff that you don't like or aren't much good at and there's no time left for the awesomeness you are ready to unleash, if only there was more time, if someone would just ask. 

Stop thinking like this. 

You are not paid to be at work, or really to do your job. Your line manager might think this way, because that's how hierarchical management works: it's essentially a system of passing goals and responsiblities down to the workforce. A nameless, interchangeable workforce. But what the executives and shareholders of your company really want from you, what they really pay you for, is Something Amazing. They don't know what it is, or what you're capable of — that's your job. Your job is to systematically hunt and break and try and build until you find the golden insight, the new play, the better way. The real challenge is how you fit the boring stuff alongside this, not the other way around.

Knock someone's socks off, then knock them back on again with these seismic beauties.Few managers will ever come to you and say, "If you think there's something around here you can transform into the most awesome thing I've ever seen, go ahead and spend some time on it." You will never get permission to take risks, commit to something daring, and enjoy yourself. But secretly, everyone around you is dying to have their socks knocked right off. Every day they sadly go home with their socks firmly on: nothing awesome today.

I guarantee that, in the process of trying to do something no-one has ever done or thought of before, you will still get the boring bits of your job done. The irony is that no-one will notice, because they're blinded by the awesome thing no-one asked you for. And their socks have been knocked off.

Modern illuminations

The illuminated manuscripts of the Middle Ages blended words and images, continuing traditions established by the Ancient Egyptians. Words and pictures go together: one without the other is a rather flat experience, like silent cinema, or eating fine food with a cold. This is why I like comic books so much. 

One of the opening sessions on Day 1 at the recent ScienceOnline conference was an hour with sketchnoter and überdoodler Perrin Ireland of Alphachimp Studio. She basically gave away all her secrets for purposeful scientific doodling. Tips like building a canon of fonts, practising icons and dividing lines, and honing an eye for the deft use of colour. 

The result... well, I had a lot of fun scribing talks. Two of them I managed to get to a point we might call al dente, or maybe half baked. The first from a session on open notebook science, something that interests me quite a bit: 

If it looks like you have to really listen and concentrate to produce one of these, that's because you do. I did miss bits, though, as I fretted over important things like what kind of robot to draw. And you might have noticed that I can't draw people. Yeah, I noticed that too. It didn't stop me adding them to the next one, from a session on the semantic web:

I'm not alone in my happiness at finding this sketchy new world. Perrin has given her perspective, and Michele Arduengo has written a lovely post about learning to draw science, and you can see many of the other efforts in this awesome Flickr gallery—the scratchings of amateurs like me sit half-convincingly alongside the professional pieces, and together I think they're rather wonderful.

Amenhotep image from Flickr user wallyg, licensed BY-NC-ND. All Flickr slideshow images are copyright of their respective creators, and may be subject to restrictions. All my work is licensed CC-BY.

The filtered earth

Ground-based image (top left) vs Hubble's image. Click for a larger view. One of the reasons for launching the Hubble Space Telescope in 1990 was to eliminate the filter of the atmosphere that affects earth-bound observations of the night sky. The results speak for themselves: more than 10 000 peer-reviewed papers using Hubble data, around 98% of which have citations (only 70% of all astronomy papers are cited). There are plenty of other filters at work on Hubble's data: the optical system, the electronics of image capture and communication, space weather, and even the experience and perceptive power of the human observer. But it's clear: eliminating one filter changed the way we see the cosmos.

What is a filter? Mathematically, it's a subset of a larger set. In optics, it's a wavelength-selection device. In general, it's a thing or process which removes part of the input, leaving some output which may or may not be useful. For example, in seismic processing we apply filters which we hope remove noise, leaving signal for the interpreter. But if the filters are not under our control, if we don't even know what they are, then the relationship between output and input is not clear.

Imagine you fit a green filter to your petrographic microscope. You can't tell the difference between the scene on the left and the one on the right—they have the same amount and distribution of green. Indeed, without the benefit of geological knowledge, the range of possible inputs is infinite. If you could only see a monochrome view, and you didn't know what the filter was, or even if there was one, it's easy to see that the situation would be even worse. 

Like astronomy, the goal of geoscience is to glimpse the objective reality via our subjective observations. All we can do is collect, analyse and interpret filtered data, the sifted ghost of the reality we tried to observe. This is the best we can do. 

What do our filters look like? In the case of seismic reflection data, the filters are mostly familiar: 

• the design determines the spatial and temporal resolution you can achieve
• the source system and near-surface conditions determine the wavelet
• the boundaries and interval properties of the earth filter the wavelet
• the recording system and conditions affect the image resolution and fidelity
• the processing flow can destroy or enhance every aspect of the data
• the data loading process can be a filter, though it should not be
• the display and interpretation methods control what the interpreter sees
• the experience and insight of the interpreter decides what comes out of the entire process

Every other piece of data you touch, from wireline logs to point-count analyses, and from pressure plots to production volumes, is a filtered expression of the earth. Do you know your filters? Try making a list—it might surprise you how long it is. Then ask yourself if you can do anything about any of them, and imagine what you might see if you could. 

Hubble image is public domain. Photomicrograph from Flickr user Nagem R., licensed CC-BY-NC-SA. 

News of the week

Some news from the last fortnight or so. Things seem to be getting going again after the winter break. If you see anything you think our readers would be interested in, please get in touch. 

Shale education

Penn State University have put together an interactive infographic on the Marcellus Shale development in Pennsylvania. My first impression was that it was pro-industry. On reflection, I think it's quite objective, if idealized. As an industry, we need to get away from claims like "fracking fluid is 99% water" and "shale gas developments cover only 0.05% of the state". They may be true, but they don't give the whole story. Attractive, solid websites like this can be part of fixing this.

New technology

This week all the technlogy news has come from the Consumer Electronics Show in Las Vegas. It's mostly about tablets this year, it seems. Seems reasonable—we have been seeing them everywhere recently, even in the workplace. Indeed, the rumour is that Schlumberger is buying lots of iPads for field staff.

So what's new in tech? Well, one company has conjured up a 10-finger multi-touch display, bringing the famous Minority Report dream a step closer. I want one of these augmented reality monocles. Maybe we will no longer have to choose between paper and digital!

Geophysical magic?

A tiny press story piqued our interest. Who can resist the lure of Quantum Resonance Interferometry? Well, apparently some people can, because ViaLogy has yet to turn a profit, but we were intrigued. What is QRI? ViaLogy's website is not the most enlightening source of information—they really need some pictures!—but they seem to be inferring signal from subtle changes in noise. In our opinion, a little more openness might build trust and help their business. 

New things to read

Sometimes we check out the new and forthcoming books in Amazon. Notwithstanding their nonsensical prices, a few caught our eye this week:

Detect and Deter: Can Countries Verify the Nuclear Test Ban? Dahlman, et al, December 2011, Springer, 281 pages, $129. I've been interested in nuclear test monitoring since reading about the seismic insights of Tukey, Bogert, and others at Bell Labs in the 1960s. There's geophysics, nuclear physics and politics in here.

Deepwater Petroleum Exploration & Production: A Nontechnical Guide Leffler, et al, October 2011, Pennwell, 275 pages, $79. This is the second edition of this book by ex-Shell engineer Bill Leffler, aimed at a broad industry audience. There are new chapters on geoscience, according to the blurb.

Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties Tiab and Donaldson, November 2011, Gulf Professional Publishing, 971 pages, $180. A five-star book at Amazon, this outrageously priced book is now in its third edition.

This regular news feature is for information only. We aren't connected with any of these organizations, and don't necessarily endorse their products or services. Low-res images of book and website considered fair use.

What do you mean by average?

I may need some help here. The truth is, while I can tell you what averages are, I can't rigorously explain when to use a particular one. I'll give it a shot, but if you disagree I am happy to be edificated. 

When we compute an average we are measuring the central tendency: a single quantity to represent the dataset. The trouble is, our data can have different distributions, different dimensionality, or different type (to use a computer science term): we may be dealing with lognormal distributions, or rates, or classes. To cope with this, we have different averages. 

Arithmetic mean

Everyone's friend, the plain old mean. The trouble is that it is, statistically speaking, not robust. This means that it's an estimator that is unduly affected by outliers, especially large ones. What are outliers? Data points that depart from some assumption of predictability in your data, from whatever model you have of what your data 'should' look like. Notwithstanding that your model might be wrong! Lots of distributions have important outliers. In exploration, the largest realizations in a gas prospect are critical to know about, even though they're unlikely.

Geometric mean

Like the arithmetic mean, this is one of the classical Pythagorean means. It is always equal to or smaller than the arithmetic mean. It has a simple geometric visualization: the geometric mean of a and b is the side of a square having the same area as the rectangle with sides a and b. Clearly, it is only meaningfully defined for positive numbers. When might you use it? For quantities with exponential distributions — permeability, say. And this is the only mean to use for data that have been normalized to some reference value. 

Harmonic mean

The third and final Pythagorean mean, always equal to or smaller than the geometric mean. It's sometimes (by 'sometimes' I mean 'never') called the subcontrary mean. It tends towards the smaller values in a dataset; if those small numbers are outliers, this is a bug not a feature. Use it for rates: if you drive 10 km at 60 km/hr (10 minutes), then 10 km at 120 km/hr (5 minutes), then your average speed over the 20 km is 80 km/hr, not the 90 km/hr the arithmetic mean might have led you to believe. 

Median average

The median is the central value in the sorted data. In some ways, it's the archetypal average: the middle, with 50% of values being greater and 50% being smaller. If there is an even number of data points, then its the arithmetic mean of the middle two. In a probability distribution, the median is often called the P50. In a positively skewed distribution (the most common one in petroleum geoscience), it is larger than the mode and smaller than the mean:

Mode average

The mode, or most likely, is the most frequent result in the data. We often use it for what are called nominal data: classes or names, rather than the cardinal numbers we've been discussing up to now. For example, the name Smith is not the 'average' name in the US, as such, since most people are called something else. But you might say it's the central tendency of names. One of the commonest applications of the mode is in a simple voting system: the person with the most votes wins. If you are averaging data like facies or waveform classes, say, then the mode is the only average that makes sense. 

Honourable mentions

Most geophysicists know about the root mean square, or quadratic mean, because it's a measure of magnitude independent of sign, so works on sinusoids varying around zero, for example. 

Finally, the weighted mean is worth a mention. Sometimes this one seems intuitive: if you want to average two datasets, but they have different populations, for example. If you have a mean porosity of 19% from a set of 90 samples, and another mean of 11% from a set of 10 similar samples, then it's clear you can't simply take their arithmetic average — you have to weight them first: (0.9 × 0.21) + (0.1 × 0.14) = 0.20. But other times, it's not so obvious you need the weighted sum, like when you care about the perception of the data points. 

Are there other averages you use? Do you see misuse and abuse of averages? Have you ever been caught out? I'm almost certain I have, but it's too late now...

There is an even longer version of this article in the wiki. I just couldn't bring myself to post it all here. 

Update on 2012-08-08 12:32 by Matt Hall

Geo-stats nut @fraserrc just reminded me that I'd missed Swanson's mean in my honourable mentions. How could I? It's a handy back-of-the-envelope estimator of the mean for a moderately skewed (usually lognormal) distribution, given P90, P50 and P10 values. It's easy, even for petroleum geologists:

It was published by retired Exxon geologist Roy Swanson, with some friends from the University of Aberdeen, and popularized by Peter Rose's courses and excellent book Risk Analysis and Management of Petroleum Exploration Ventures.

Reference

Hurst, A, G Brown, and R Swanson (2000). Swanson's 30–40–30 rule. AAPG Bulletin 84 (12), p 1883–1891. DOI 10.1306/​8626C70D-173B-11D7-8645000102C1865D