This week, Luke take a look at the differences between stochastic and scenario-based uncertainty methods, and how the underlying question is one of precision versus accuracy.
Here is a shot of Luke's artwork, without his mug getting in the way!
Hello, and welcome back to the Cognitive Whiteboard - where I practice my art skills, and share my experiences in applying subsurface best practices to oilfield decision making.
My name is Luke and today, I would like to talk about the differences between stochastic and scenario-based uncertainty methods. In doing so, we will explore the reasons why some of the industry’s leading oil companies prefer to use the latter when it comes to characterising the economic risk associated with developing their assets.
Precision vs. Accuracy Defined
Before we get underway let's quickly revisit the difference between precision and accuracy, as it's relevant to this discussion.
Precision is the degree of repeatability of an estimate: the size of the cluster on the dartboard, the random error that you cannot avoid.
Accuracy, on the other hand, is the difference between the average of the estimates and the actual answer.
In the oilfield, precisely accurate is usually unobtainable. Precisely wrong must be avoided at all costs: it encourages over-confidence, and leads to economic train wrecks. Approximately accurate, in contrast, is often perfectly suitable for making robust business decisions.
And so, when it comes to managing uncertainty, in geomodelling, which do we tend to focus on: precision or accuracy?
Which Is Which?
Well, stochastic theory was developed to address random errors: in geomodelling stochastic methods allow you to vary the input coefficients and test their impact on the answer. Programmatically, this is very easy for software engineers to implement. In object modelling, for example, we can easily assign uncertainty to the parameters controlling channel sinuosity and channel size.
Does this address accuracy? In many ways, it does not.
Changes to these parameters will tend to influence fluid velocity in a simulation model but it may leave the fundamental connectivity of the simulation model broadly unchanged: it explores the precision behind your model concept. As geologists, however, we need the ability to test the economic impact of major assumptions controlling reservoir connectivity.
Avoiding Train Wrecks
Was our reservoir deposited in an upper-fan or lower-fan setting? What impact would this have on off-channel sands connectivity?
Stochastic methods are not particularly powerful at exploring these kinds of uncertainties. To investigate high-level assumptions, we need to develop alternative geomodel scenarios and carry these into simulation. We need to see the economic impact of different connectivities between injector-producer pairs
Likening a simulation to a chaotic plumbing diagram, if we just change pipe diameter, or adjust flow rate, we would not change which faucets are connected. And with many simulation results showing similar answers, we may become begin to believe that the geological uncertainties bear little impact on our field development plan.
And so, whilst stochastic methods help us explore precision, it is critical that you carry scenario-based uncertainties into simulation as well - to investigate the impact of systematic unknowns on field economics - and in doing so allow you to improve the accuracy of your predictions and avoid economic train wrecks.
Thank you very much; I hope you enjoyed this video, and I welcome your comments.