Chances and values of input parameters have to be estimated. Probability estimates are usually a subjective estimate based on personal experience. But factual experience in databases may refine such estimates. Also bayesian statistical methods deliver better, more realistic estimates of chances and input variables. Prospect appraisal should facilitate this by providing a formal framework.
Extracting maximum information from data
A sophisticated prospect appraisal method can obtain the maximum relevant information out of a wealth (more often scarcity) of geological and geophysical information. Thus it helps to improve the quality of judgment about the prospect at hand, but also helps to consolidate knowledge about the play (Doust, 2009).
As the appraisal forms a clear statement what people believe at a given moment, and undersign the evaluation, they know that they are more or less accountable. This may enhance an individual's learning process. He can go back to any judgements made and see whether at the time he could have done better.
Uniformity of reporting
Having a company-wide system for evaluation means that management and staff are communicating about prospects in an easier and more precise way than if only subjective statements are made ("seat of the pants" judgements, which are possibly too personal).
Having files with prospect appraisals allows easy access to data for hindsight analysis. Lack of time will usually be a good excuse not to do any hindsight analysis, especially of dry holes! The prospect appraisal file will enable explorationists to learn about the validity of their input statements (measure the so-called "input bias", if any) and the staff responsible for the appraisal system will learn about the validity of their calibrations and model ("calibration bias", if any).
The main aim, of course, remains generating numbers that are the translation of an uncertain situation into a form that can be used in an economic evaluation of risks and rewards.