In a previous post, we defined chance as the probability of a given event. In contrast, the Storage Resources Management System (SRMS) defines chance as the probability of gain or success. But what about the probability of loss or failure? In our view, the question “how could my project fail?” is actually more relevant than the question, “how could my project succeed?”
A focus on successful analogues reinforces the use of heuristics (rules of thumb) and nurtures our intuition. This distorts our objectivity and makes us more vulnerable to biases which can skew our assessments. Some of the more common biases we have observed include anchoring (relying too heavily on a single analogue or dataset), confirmation (searching for and interpreting data that confirm our beliefs), overconfidence (overestimating the accuracy of one’s analysis or interpretation), and motivational bias (actions driven by a desire for personal gain).
As an example of their impact, Kahneman and Tversky (1982) showed that in complex systems that require a series of events to succeed, biases such as anchoring can lead to the underestimation of the probability of failure. Even if the individual chance of failure for each event is low, and the impact of bias is small, the aggregated impact on the chance of failure may be significant. Focusing on potential failure modes and applying a risk assessment workflow that mitigates bias is therefore important for evaluating long-term, low-frequency, high-impact events.
Focusing on the chance of failure highlights the importance of evaluating injection projects that experienced significant unforeseen events. An understanding of what caused these events provides important lessons that should be applied when planning for future SCS projects. But it’s important to remember that events, if they occur, may or may not be hazards. For example, a single seismic event of less than a magnitude of 2 on the Richter scale is very unlikely to be a hazard. However, swarms of these small events can and do become real hazards to project continuation.
Reference: Kahneman, D. and Tversky, A. 1982. Judgement under uncertainty: Heuristics and biases. In: Kahneman, D., Slovic, P. and Tversky, A. (eds) Judgement under uncertainty: Heuristics and biases. Cambridge University Press, New York, 3-20.