Typically, the Monte Carlo simulation (MCS) approach was generally applied to assess and quantify uncertainty in probabilistic reserve estimates and improve risk decision making, regrdless of that it can be quite computationally intensive, MCS method has the advantages of generating possible outcomes That contain more information relative to deterministic and scenario approach by taking into consideration the uncertainty associated with the range of various input variable. However, MCS entails that the probability distribution functions of all uncertain input parameter be entirely known, this might be an obstacle or limitation on successful implementation of MCS as subjectivity on the selection and definition of the input variable distributions and their characterization or due to incomplete information and lack of data will critically impact and limit both its proper application and interpretation of the results and consequently doubt statistically the robustness of the solution. This shortcoming of MCS and difficulty can be circumvented by other complementary method like the PEM, first-order second moment (FOSM) or Warren’s probability analysis methods which does not require a previous knowledge of the range and distribution shape to be defined or condition where information concerning uncertain parameter is not sufficient or reliable, moreover it has the advantage of less computational requirement to attain solution with comparable accuracy. This paper provides a comparative assessment of Point Estimate Method (PEM) for analyzing uncertainty of hydrocarbon in place as more practical alternative approaches to Monte Carlo simulation (MCS). Five PEM Rosenblueth, Harr, Che-Hao et al., Geethanjali et al. and Hong 2n scheme algorithm were used to model output uncertainty of hydrocarbon in Place for more than 20 field and predict P10, P50 and P90 using different PEM Technique. MSC result was generated for the same fields with an optimum number of samples for key variable using stability analysis and statistical measure of model run convergence which are benchmark to which the PEMs’ results are compared.