Based on the characteristics of oil and gas reserve, value classification of oil and gas reserve was assessed. Seven factors influencing value classification of oil and gas reserve were chosen, which were reserve scale, reserve abundance, reserve depth, oil viscosity, permeability, freezing point and recovery ratio. Least square support vector machine model was applied to simulate value degradation of oil and gas reserve. The parameters of penalty factor C and kernel function parameter σ can be decided by grid searching method. The results show that least square support vector machine is a valid method in the value classification of oil and gas reserve. The right rate of training is up to 95% and the right rate of testing is up to 81%.%针对油气储量的特点,对油气储量价值的优劣等级进行划分.选取影响油气储量价值等级的7个因素,即储量规模、储量丰度、储层埋深、原油黏度、渗透率、凝固点和采收率,采用最小二乘支持向量机模型对油气储量价值等级划分进行仿真,并运用网格搜索法确定最小二乘支持向量机模型的参数惩罚因子C和核函数参数σ.结果表明,最小二乘支持向量机是评价油气储量价值等级的有效方法,训练正判率达到95％,检验正判率达到81％.
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