Abstract: The margin and condition probability analysis is broadly applied to the geological risk evaluation for an immature play with joining the "success" probabilities subjectively specified for those independently involved geological factors. Considering that it is difficult to specify a reasonable scoring distribution curve for each geological factor, this method contributes a "point" estimation about whether there exists petroleum resource. Obviously, subjected to lack efficient ways to encode the information about geological multi-scene of subsurface and possibi-lities for each geological factor, the above crisp estimation conclusion would be generally either higher or lower. In order to enhance the capability of uncertainty expression of geological risk evaluation, this paper presents three heuristic mathematic models to deeply quantify the understanding of a geological expert and objectively delineate the possibilities of subsurface occasion, etc. Meanwhile, the uncertainty assessment methods discussed also de-monstrate a reasonable uncertainty evaluation process from subjective guess to objective prediction in order to shrink the uncertainty of evaluation as more as possible. Firstly, the double linear conversion between subjective inference for each factor and quantized confidence shares an efficient alternative to describe subjective uncertainty. Next, the specification of multi-value model and setup of corresponding fuzzy rules for each geological factor may accurately and honestly reflect the worldly uncertainty of subsurface multi-scene while matching the domain expert’s understanding as more as possible. At last, Montecarlo method randomly joins the objective uncertainty distribution curve of each factor and shares experts with quantiles evaluation which would benefit incoming reasonable exploration solution. As a conclusion, this paper not only investigates how to make full scale uncertainty evaluation for geological evaluation, but also expands a new horizon about geological risk uncertainty research.