储量计算的一个重要参数是矿石的品位。
The grade of ores is one important parameter for reserve calculation.
缝洞型潜山油藏具有特殊的储集类型和特征，其饱和度参数解释、有效厚度划分、储层平面分布的确定是储量计算中的难题。
The interpretation of fracture and vuggy saturation, the determination of effective thickness and the distribution of reservoir are the main problems in reserve calculation.
论述了蒙特卡洛方法的数学原理，并结合实例阐述了在天然气储量计算过程中各计算参数实际分布产生随机变量抽样方法。
This paper expounds the mathematical theory of Monte Carlo method, and the random variable sample method of parameters in the calculation process of gas reserves in conjunction with examples.
在储量计算的有关参数中，有效储层下限的确定直接影响有效储层厚度、孔隙度和含气饱和度三个参数。
The corroboration of effective reservoir lower limit in reserves calculation respects parameters influence effective reservoir thickness, porosity and gas saturation directly.
采用BP神经网络技术计算煤层气储层物性参数和含气量，较好的满足了煤层气储量计算及开发部署对解释精度的要求。
Using BP neural network computing CBM reservoir parameters, gas content, it's better to meet the calculation of CBM reservoir and interpretation accuracy for development.
储层孔隙度是容积法储量计算的基本参数，而准确确定地层条件下的孔隙度值是提高储量计算精度的基础。
Since porosity is a basic parameter for the calculation of gas reservoir reserves, determining the porosity under the reservoir condition is the only way to enhance the precision of the calculation.
利用储量参数的随机网格模型直接进行储量计算，解决了传统容积法计算复杂油藏储量时储量参数难以取准的问题。
The method can be used to deal with the issues that the reserve parameters are difficult to correctly acquire when estimating complex reservoir's reserves in terms of traditional volume method.
用传统容积法计算储量时，由于对储层参数采用的是平均值而忽略了储层的非均质因素，影响了储量计算的准确性。
When we calculate reserves with traditional cubature, parameters of reservoir bed we use are average values. This ignores anisotropic factors of reservoirs, so that affects accuracy of calculate.
用传统容积法计算储量时，由于对储层参数采用的是平均值而忽略了储层的非均质因素，影响了储量计算的准确性。
When we calculate reserves with traditional cubature, parameters of reservoir bed we use are average values. This ignores anisotropic factors of reservoirs, so that affects accuracy of calculate.
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