Recently it was noted that there are over ten times more bacterial and fungal cells on or in the average human - than there are human cells! (http://en.wikipedia.org/wiki/Human_flora)
Bob goes on to say, “Look, I wish it weren’t so. All I do is look at the FACTS. I’d be happy to go over this with anyone. I truly wish I was wrong.” What could be more reasonable?
Along comes Sam. Sam is a noted biologist with a long, distinguished career. In fact, Sam is currently director of the biological survey for his adopted state. Sam’s state mainly has birds, not humans. Over the years though, Sam has known a few humans and he’s found them, well … a little weird! Sam’s impressed with Bob, and believes he’s telling the truth. Sam recommends Bob and Bob’s work, and he goes on to exclaim, “Y’all better pay attention to what Bob has to say.” And so it goes that Bob’s credibility grows, and it’s not looking good for humans remaining to be classified as humans …
Who’s telling the truth here? What are the facts? Are humans, humans? Or are they bugs and mold?
As you can see, statistics don’t always tell the whole story. In fact, in this complicated world we can get badly misled if we limit our analyses to simple, reductionist statistics. Statistics can be quite helpful – but only if they are used in parallel with a full and correct understanding of all the available objective and subjective data.
Unfortunately, in this complicated world none of us has time to become “expert” on all things. As a result, we are forced to rely on simplifications or summaries done by others. Problems can ensure if those we are relying on have not gathered and correctly analyzed the requisite data, but instead have made statistical observations based on incomplete data or analyses. Further, in some cases it may not even be possible to come to a correct conclusion – even with all of the available data and the best analysis! The danger of statistics lies (pun intended) in the fact that statistics ALWAYS yield a numerical answer – an answer which may be premature, convoluted or just plain wrong.
So, statistics are not facts at all; rather, statistics are just numbers that may - or may not - represent a realistic analysis of a situation.
The Complexity of Shale Plays
Various rock parameters are required in order for a geographical portion of a given of a shale play to “work”. Just like the rock you see on the side of the road, these parameters vary across an area. We humans tend to subdivide things into convenient, already-existing “buckets”. In the case of land areas, counties are already defined, so we might say Blah County is “good”, and Blah Blah County is “not good”. The fact is, even in a play where 40 counties are “good”, there are always the “edges” - with “edges” representing the transitions between “good” and “not good”.
The noted Marcellus Shale expert, Dr. Terry Engelder of Penn State, has come up with the useful analogy of “toast” to explain the important shale gas geochemical parameter known as thermal maturity (sometimes referred to as Ro or Tm). In his slide show, Dr. Engelder shows partially cooked toast, charred toast, and toast that looks like it was cooked “just right”. The genius of this analogy is that we can all relate to properly cooked toast!
As it turns out, rock parameters don’t necessarily honor the county lines we have drawn. So, on the “edges” of a play part of a county may be perfectly cooked toast, and part of it may be burned toast! How do we find these edges? Sometimes data exists, but often it is spotty or undependable for whatever reason. Sometimes two analysts can look at the same data and come up with two different answers. Sometimes two analysts can come up with … five different answers! Also, rock parameters don’t follow straight lines on a map; they curve around – in here, out there.
So, often we have to find the edges the hard, expensive way - we have to drill multi-million dollar wells. And sometimes even when Company X thinks they know where the edge is, Company Y doesn’t have that same data. So, Company Y may wind up drilling a non-commercial well in an area that Company X had already condemned as “not good”. In this case, Company Y is viewed with disfavor. Given the full alphabet of companies out there, and given the infinitely curvy lines of rock parameters, there can be a lot of failed efforts along an “edge”. All these failed efforts can go into a statistical exercise that views a given portion of a given shale play in a simplistic way. Should the play in general, and Company X in particular, be condemned for the actions of Company Y and its alphabet brethren?
On the other hand, sometimes Company Y may have the same data as Company X, but Company Y thinks the data is suspect, or incomplete, or just has a different interpretation. So, in this case, Company Y takes the risk and drills a successful, multi-million dollar well. Company Y becomes a hero and in fact by extending the play in this direction, Companies Z, A, B - and even Company X - may benefit! Meanwhile, the royalty owners in this new area and the schoolchildren of that state (who sometimes get the benefits from the production taxes paid by Company Y and its royalty owners) become major beneficiaries of Company Y’s “guts”. Such is the nature of the oil and gas exploration business in unconventional shale plays!