The assessment results of prevailing statistical methods used in the petroleum industry are

to some extent

ina-dequate in helping the formulation of effective exploration strategies.There are still plenty of rooms of improvement in terms of assessment methods.Aiming at enhancing the economy of commercial discoveries and dealing with challenges faced during exploration deployment planning

we proposed that:(1) Differentiating the applicable scope and conditions of marginal

conditional and spatial probability mathematical assessment models and providing information concerning geological-constraint risk so as to improve success rate of exploratory drilling in less-explored areas;(2) Providing more objective data samples for solving general reservoir scale distribution models by delineating highly-explored areas with the guidance of petroleum accumulation theories and sorting and merging reserve assessment units into oil and gas reservoirs (samples) that went through the same geologic processes according to the time order of first discovery well;(3) Taking into consideration the effect of different exploratory investments upon oil and gas discoveries and eliciting expectation maximization algorithm to work out the Pareto distribution parameters based on exploration efficiency plates

so as to predict more objectively the resource potential of oil and gas and the structure of reservoir scales;(4) Using scenario trees representing respectively geological and market uncertainties and geology-or strategy-related Bayesian networks to combine effectively and organically investment portfolios and exploration strategies.