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Sunday, 24 November 2013

20 Things politicians should understand ... (Part 2)

Continuing the previous posting here are 5 more "Things politicians need to know about shale gas science", inspired by the recent Guardian article entitled "Top 20 things politicians need to know about science" from an original article in Nature.  

It is not just politicians that need to know this stuff - without it the whole debate is not possible.

6. Regression to the mean can mislead

Extreme patterns in data are likely to be, at least in part, anomalies attributable to chance or error. The next count is likely to be less extreme. There is the tendency in any debate where passions run high and positions are entrenched for either side to grab hold of extreme data and either plug it or lambast it depending on whether it supports their position or not. This is not a rational scientific approach.

The Cuadrilla drilling in Lancashire caused, it is generally agreed, two small earthquakes (magnitudes 2.3 and 1.5). It would not be reasonable to take this observation as being typical of what will happen in all cases of drilling and hydraulic fracturing. In fact, it is likely that most hydraulic fracturing will not cause even earthquakes of this magnitude. On the other hand, if sufficient hydraulic fracturing operations were to be carried out there would be rare occasions when larger earthquakes will be triggered. That is the reason why the government has instituted a traffic light system which sets the threshhold for the freezing of operations at a low magnitude (M=0.5), which is an earthquake that is 32 times smaller than the smaller of the two Lancashire earthquakes and 58 times smaller than the larger.

7. Extrapolating beyond the data is risky

Patterns found within a given range do not necessarily apply outside that range. The range maybe a measurement or may be a location.

In the first case it may simply be that if we calculate that a well corrodes at a certain rate over 1 year, it is not necessarily the case that it will corrode five times as much over 5 years. It might be significantly less! It might me significantly more!!

In the second case, and it should be reasonably obvious, it is not possible to apply observations made in the USA with predicted causes in the UK or elsewhere in Europe, or in fact anywhere other than close to where the original observations were made: The rocks are different, their properties are different, the temperature and pressure is different, the working practices are different and so on.

8. Beware the base-rate fallacy

This is a more technical point. The ability of an imperfect test to identify something depends upon the likelihood of its occurrence (the base rate). For example, an image log (one of the measurement tools that is placed in a well) might be able to identify fractures in the rock with a 99% accuracy, and might identify active fractures in this way, yet it might still be unlikely that the fractures will reactivate when hydraulically fractured.

9. Controls are important

A control group is dealt with in exactly the same way as the experimental group, except that the treatment is not applied. Without a control, it is difficult to determine whether a given treatment really had an effect.

This is really important in understanding most of the existing studies related to hydraulic fracturing. For example, there are no studies of aquifer contamination from the USA where the aquifer water was systematically measured before the hydraulic fracturing started. Hence, it is impossible to say whether any measured contamination after hydraulic fracturing is due to or related to that drilling or whether it pre-existed. That is a fundamental fact of science. Without the initial background levels acting as a control, the post fracturing measurements are meaningless from the point of view of attributing the source of the contamination.

10. Randomisation avoids bias

Experiments should, wherever possible, allocate individuals or groups to interventions randomly. This is a statistical ideal that is difficult to apply in geoscience. We cannot randomly chose a location to do the drilling or hydraulic fracturing because we need to drill where we think there is some chance of success.  It would not be useful, for example, to drill in Brent, where there is no shale gas, despite Brent's recent political posturing.

However, when tests are done, it is important that the companies take account of any reason why their location might not be 'typical' such that it gives odd or extreme values. For example, companies should not drill where there is a known set of major faults, whether they are seismically active or not. In fact it is in the companies' interest not to do this for a whole raft of practical, financial, safety and public relations reasons.

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