This post will illustrate how to make a rough estimate of the dollar impact data entry errors have on common crude testing measurements. It is part of our series on estimating human errors in crude quality and measurement. It can be easy for an oil company to invest too little in reducing human errors, not because they are not important, but because they are hard to measure. The examples in the series outline rough calculations measurement supervisors can use to decide whether a specific source of human error is worth further investigation.
The short answer is: a lot. This explains why oil companies everywhere in the supply chain spend so much on quality and measurement, from sophisticated on-line analyzers and automatic samplers at most custody transfer points, to large annual third-party-lab budgets.