What is AVO-friendly processing?

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AVO-friendly processing gets called various things: true amplitude, amplitude-friendly, and controlled amplitude, controlled phase (or just 'CACP'). And, if you've been involved in any processing jobs you'll notice these phrases get thrown around a lot. But seismic geophysics has a dirty little secret... we don't know exactly what it is. Or, at least, we can't agree on it.

A LinkedIn discussion in the Seismic Data Processing group earlier this month prompted this post:

I can't compile a list of exactly which processes will harm your AVO analysis (can anyone? Has anyone??), but I think I can start a list of things that you need to approach with caution and skepticism:

  • Anything that is not surface consistent. What does that mean? According to Oliver Kuhn (now at Quantec in Toronto):
Surface consistent: a shot-related [process] affects all traces within a shot gather in the same way, independent of their receiver positions, and, a receiver-related [process] affects all traces within a receiver gather in the same way, independent of their shot positions.
  • Anything with a window — spatial or temporal. If you must use windows, make them larger or longer than your areas and zones of interest. In this way, relative effects should be preserved.
  • Anything that puts the flattening of gathers before the accuracy of the data (<cough> trim statics). Some flat gathers don't look flat. (The thumbnail image for this post is from Duncan Emsley's essay in 52 Things.)
  • Anything that is a sort of last resort, post hoc attempt to improve the data — what we might call 'cosmetic' treatments. Things like wavelet stretch correction and spectral shaping are good for structural interpreters, but not for seismic analysts. At the very least, get volumes without them, and convince yourself they did no harm.
  • Anything of which people say, "This should be fine!" but offer no evidence.

Back to my fourth point there... spectral shaping and wavelet stretch correction (e.g. this patented technique I was introduced to at ConocoPhillips) have been the subject of quite a bit of discussion, in my experience. I don't know why; both are fairly easy to model, on the face of it. The problem is that we start to get into the sticky question of what wavelets 'see' and what's a wavelet anyway, and hang on a minute why does seismic reflection even work? Personally, I'm skeptical, especially as we get more used to, and better at, looking at spectral decompositions of stacked and pre-stack data.

Divergent paths

I have seen people use seismic data with very different processing paths for structural interpretation and for AVO analysis. This can happen on long-term projects, where the structural framework depends on an old post-stack migration that was later reprocessed for AVO friendliness. This is a bad idea — you won't be able to put the quantitative results into the structural framework without introducing substantial error.

What we need is a clinical trial of processing algorithms, in which they are tested against a known model like Marmousi, and their effect on attributes is documented. If such studies exist, I'd love to hear about them. Come to think of it, this would make a good topic for a hackathon some day... Maybe Dallas 2016?

The blind geoscientist

Last time I wrote about using randomized, blind, controlled tests in geoscience. Today, I want to look a bit closer at what such a test or experiment might look like. But before we do anything else, it's worth taking 20 minutes, or at least 4, to watch Ben Goldacre's talk on the subject at Strata in London recently:

How would blind testing work?

It doesn't have to be complicated, or much different from what you already do. Here’s how it could work for the biostrat study I mentioned last time:

  1. Collect the samples as normal. There is plenty of nuance here too: do you sample regularly, or do you target ‘interesting’ zones? Only regular sampling is free from bias, but it’s expensive.
  2. Label the samples with unique identifiers, perhaps well name and depth.
  3. Give the samples to a disinterested, competent person. They repackage the samples and assign different identifiers randomly to the samples.
  4. Send the samples for analysis. Provide no other data. Ask for the most objective analysis possible, without guesswork about sample identification or origin. The samples should all be treated in the same way.
  5. When you get the results, analyse the data for quality issues. Perform any analysis that does not depend on depth or well location — for example, cluster analysis.
  6. If you want to be really thorough, the disinterested party to provide depths only, allowing you to sort by well and by depth but without knowing which wells are which. Perform any analysis that doesn’t depend on spatial location.
  7. Finally, ask for the key that reveals well names. Hopefully, any problems with the data have already revealed themselves. At this point, if something doesn’t fit your expectations, maybe your expectations need adjusting!

Where else could we apply these ideas?

  1. Random selection of some locations in a drilling program, perhaps in contraindicated locations
  2. Blinded, randomized inspection of gathers, for example with different processing parameters
  3. Random selection of wells as blind control for a seismic inversion or attribute analysis
  4. Random selection of realizations from geomodel simulation, for example for flow simulation
  5. Blinded inspection of the results of a 'turkey shoot' or vendor competition (e.g. Hayles et al, 2011)

It strikes me that we often see some of this — one or two wells held back for blind testing, or one well in a program that targets a non-optimal location. But I bet they are rarely selected randomly (more like grudgingly), and blind samples are often peeked at ('just to be sure'). It's easy to argue that "this is a business, not a science experiment", but that's fallacious. It's because it's a business that we must get the science right. Scientific rigour serves the business.

I'm sure there are dozens of other ways to push in this direction. Think about the science you're doing right now. How could you make it a little less prone to bias? How can you make it a shade less likely that you'll pull the wool over your own eyes?

Experimental good practice

Like hitting piñatas, scientific experiments need blindfolds. Image: Juergen. CC-BY.I once sent some samples to a biostratigrapher, who immediately asked for the logs to go with the well. 'Fair enough,' I thought, 'he wants to see where the samples are from'. Later, when we went over the results, I asked about a particular organism. I was surprised it was completely absent from one of the samples. He said, 'oh, it’s in there, it’s just not important in that facies, so I don’t count it.' I was stunned. The data had been interpreted before it had even been collected.

I made up my mind to do a blind test next time, but moved to another project before I got the chance. I haven’t ordered lab analyses since, so haven't put my plan into action. To find out if others already do it, I asked my Twitter friends:

Randomized, blinded, controlled testing should be standard practice in geoscience. I mean, if you can randomize trials of government policy, then rocks should be no problem. If there are multiple experimenters involved, like me and the biostrat guy in the story above, perhaps there’s an argument for double-blinding too.

Designing a good experiment

What should we be doing to make geoscience experiments, and the reported results, less prone to bias and error? I'm no expert on lab procedure, but for what it's worth, here are my seven Rs:

  • Randomized blinding or double-blinding. Look for opportunities to fight confirmation bias. There’s some anecdotal evidence that geochronologists do this, at least informally — can you do it too, or can you do more?
  • Regular instrument calibration, per manufacturer instructions. You should be doing this more often than you think you need to do it.
  • Repeatability tests. Does your method give you the same answer today as yesterday? Does an almost identical sample give you the same answer? Of course it does! Right? Right??
  • Report errors. Error estimates should be based on known problems with the method or the instrument, and on the outcomes of calibration and repeatability tests. What is the expected variance in your result?
  • Report all the data. Unless you know there was an operational problem that invalidated an experiment, report all your data. Don’t weed it, report it. 
  • Report precedents. How do your results compare to others’ work on the same stuff? Most academics do this well, but industrial scientists should report this rigorously too. If your results disagree, why is this? Can you prove it?
  • Release your data. Follow Hjalmar Gislason's advice — use CSV and earn at least 3 Berners-Lee stars. And state the license clearly, preferably a copyfree one. Open data is not altruistic — it's scientific.

Why go to all this trouble? Listen to Richard Feynman:

The first principle is that you must not fool yourself, and you are the easiest person to fool.

Thank you to @ToriHerridge@mammathus@volcan01010 and @ZeticaLtd for the stories about blinded experiments in geoscience. There are at least a few out there. Do you know of others? Have you tried blinding? We'd love to hear from you in the comments! 

Checklists for everyone

Avoidable failures are common and persistent, not to mention demoralizing and frustrating, across many fields — from medicine to finance, business to government. And the reason is increasingly evident: the volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely, or reliably. Knowledge has both saved and burdened us.

I first learned about Atul Gawande from Bill Murphy's talk at the 1IWRP conference last August, where he offered the surgeon's research model for all imperfect sciences; casting the spectrum of problems in a simple–complicated–complex ternary space. In The Checklist Manifesto, Gawande writes about a topic that is relevant to all all geoscience: the problem of extreme complexity. And I have been batting around the related ideas of cookbooks, flowcharts, recipes, and to-do lists for maximizing professional excellence ever since. After all, it is challenging and takes a great deal of wisdom to cut through the chaff, and reduce a problem to its irreducible and essential bits. Then I finally read this book.

The creation of the now heralded 19-item surgical checklist found its roots in three places — the aviation industry, restaurant kitchens, and building construction:

Thinking about averting plane crashes in 1935, or stopping infections in central lines in 2003, or rescuing drowning victims today, I realized that the key problem in each instance was essentially a simple one, despite the number of contributing factors. One needed only to focus attention on the rudder and elevator controls in the first case, to maintain sterility in the second, and to be prepared for cardiac bypass in the third. All were amenable, as a result, to what engineers call "forcing functions": relatively straightforward solutions that force the necessary behavior — solutions like checklists.

What is amazing is that it took more than two years, and a global project sponsored by the World Health Organization, to devise such a seemingly simple piece of paper. But what a change it has had. Major complications fell by 36%, and deaths fells by 47%. Would you adopt a technology that had a 36% improvement in outcomes, or a 36% reduction in complications? Most would without batting an eye.

But the checklist paradigm is not without skeptics. There is resistance to the introduction of the checklist because it threatens our autonomy as professionals, our ego and intelligence that we have trained hard to attain. An individual must surrender being the virtuoso. It enables teamwork and communication, which engages subordinates and empowers them at crucial points in the activity. The secret is that a checklist, done right, is more than just tick marks on a piece of paper — it is a vehicle for delivering behavioural change.

I can imagine huge potential for checklists in the problems we face in petroleum geoscience. But what would such checklists look like? Do you know of any in use today?

Bad Best Practice

Applied scientists get excited about Best Practice. New professionals and new hires often ask where 'the manual' is, and senior technical management or chiefs often want to see such documentation being spread and used by their staff. The problem is that the scientists in the middle strata of skill and influence think Best Practice is a difficult, perhaps even ludicrous, concept in applied geoscience. It's too interpretive, too creative.

But promoting good ideas and methods is important for continuous improvement. At the 3P Arctic Conference in Halifax last week, I saw an interesting talk about good seismic acquisiton practice in the Arctic of Canada. The presenter was Michael Enachescu of MGM Energy, well known in the industry for his intuitive and integrated approach to petroleum geoscience. He gave some problems with the term best practice, advocating instead phrases like good practice:

  • There's a strong connotation that it is definitively superlative
  • The corollary to this is that other practices are worse
  • Its existence suggests that there is an infallible authority on the subject (an expert)
  • Therefore the concept stifles innovation and even small steps towards improvement

All this is reinforced by the way Best Practice is usually written and distributed:

  • Out of frustration, a chief commissions a document
  • One or two people build a tour de force, taking 6 months to do it
  • The read-only document is published on the corporate intranet alongside other such documents
  • Its existence is announced and its digestion mandated

Unfortunately, the next part of the story is where things go wrong:

  • Professionals look at the document and find that it doesn't quite apply to their situation
  • Even if it does apply, they are slightly affronted at being told how to do their job
  • People know about it but lack the technology or motivation to change how they were already working
  • Within 3 years there is enough new business, new staff, and new technology that the document is forgotten about and obselete, until a high-up commissions a document...

So the next time you think to yourself, "We need a Best Practice for this", think about trying something different:

  • Forget top-down publishing, and instead seed editable, link-rich documents like wiki pages
  • Encourage discussion and ownership by the technical community, not by management
  • Request case studies, which emphasize practical adaptability, not theory and methodology
  • Focus first on the anti-pattern: common practice that is downright wrong

How do you spread good ideas and methods in your organization? Does it work? How would you improve it?