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目前低渗透储层测井评价结果与试油效益并不令人满意 ,其原因是低渗透储集层类型的差异导致其导电性和测井响应特征不同 ,而测井解释大多数还是沿用中高孔渗油气藏的解释方法。因此 ,低渗透储集层的测井解释模型要针对不同的储集层类型建立模型 ,其核心问题是利用测井资料进行低渗透储集层类型的识别。文章首先应用毛细管压力曲线、岩电实验数据、物性分析数据和相对渗透率曲线等实验资料 ,结合实际测井和测试资料 ,采用地质储层分类标准和储层的分形特征对塔中志留系储层进行分类研究 ,建立了适应井区的测井分类标准 ;然后 ,采用BP神经网络方法建立了识别储层类型的测井分类模型 ;最后应用该方法处理该层系低孔低渗储层的实际测井资料。其处理解释结果与岩心分析结论比较 ,符合率高达 91.89% ,试油结果也验证了该方法的实用性、准确性。
At present, the evaluation results of low permeability reservoir logging and test oil are unsatisfactory, because the difference of low permeability reservoirs results in different conductivity and logging response characteristics, while the majority of well logging interpretation is still in high Holes Permeability Reservoir Interpretation Method. Therefore, the log interpretation model for low-permeability reservoirs needs to model different types of reservoirs. The core issue is the use of well logging data to identify types of low-permeability reservoirs. In this paper, the capillary pressure curve, the experimental data of rock electric, the petrophysical analysis data and the relative permeability curve are used in this paper. Based on the actual logging and testing data, the geologic reservoir classification criteria and reservoir fractal characteristics The classification of reservoirs has been studied and well logging classification standards have been established. Secondly, a well logging classification model that identifies reservoir types has been established by using BP neural network method. Finally, this method is applied to deal with low porosity and low permeability reservoirs The actual logging data. The results of its processing and core analysis compared to conclusions, the coincidence rate as high as 91.89%, the test results also verify the practicality and accuracy of the method.