混合模拟退火遗传和随机森林构建复杂碳酸盐岩储层渗透率测井解释模型
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张亚男(1995—),女,山西吕梁人,在读硕士研究生,从事测井数据处理与综合解释研究。E-mail:1594547623@qq.com。

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国家自然科学基金项目“致密气储层岩石导电机理研究及饱和度评价”(41404084),国家科技重大专项子课题“复杂碳酸盐岩储层测井评价关键技术研究与应用”(2017ZX05032-003-005)。


Logging interpretation model on complex carbonate reservoir permeability based on hybrid simulated annealinggenetic algorithm-random forest algorithm
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    摘要:

    伊拉克H油田M层组巨厚型碳酸盐岩储层非均质性强、孔隙类型复杂,常规渗透率测井解释模型适用性差。 为此,提出基于常规测井资料及衍生参数的混合模拟退火遗传随机森林算法(SA-GA-RF)渗透率评价模型。从测井响应特征分析出发,确定渗透率敏感曲线,通过随机森林算法(RF)建立基于地球物理测井资料的渗透率评价模型,并利用模拟退火遗传算法(SA-GA)对RF中的参数进行寻优,消除RF中关键参数对模型精度的影响。应用该方法对研究区进行渗透率评价,与RF、优化后的BP神经网络预测结果进行对比,结果表明,基于SA-GA-RF构建的复杂碳酸盐岩储层渗透率评价模型既能充分利用常规测井曲线的响应特征,又能表征测井曲线随深度变化的趋势,在非均质性强的碳酸盐岩储层中有很好的适用性。相比优化的BP神经网络,SA-GA-RF模型预测的准确度明显提高,与岩心渗透率的相关性达0.83,比RF的评价精度提高了0.15。

    Abstract:

    Because of the strong heterogeneity and complex pore types of the extremely thick carbonate reservoir in M Formation of H Oilfield in Iraq,the applicability of conventional permeability logging interpretation models is poor. To solve this problem,this paper proposes a hybrid simulated annealing-genetic algorithm-random forest(SA-GA-RF)algorithm permeability evaluation model with conventional logging data and derived parameters. Depending on the analysis of logging response characteristics,the permeability sensitive curve is determined,and the permeability evaluation model based on the geophysical logging data is constructed by a random forest(RF)algorithm. The simulated annealing-genetic algorithm (SA-GA)is used to optimize the parameters in the RF model,which thus eliminates the influence of key parameters in the RF algorithm on the model accuracy. This method is applied to evaluate the permeability of the study block,and the prediction results are compared with those of RF and the improved back-propagation(BP)neural network. The results show that the SA-GA-RF model for the permeability evaluation of complex carbonate reservoirs can take full advantage of the response characteristics of the conventional logging curves and reflect the trend of logging curves changing with depth. Particularly,it has good applicability in carbonate reservoirs with strong heterogeneity. Compared with the improved BP neural network,the SA-GA-RF model has distinctly enhanced accuracy. The correlation between the core permeability and the prediction result is up to 0.83,which is 0.15 higher than the accuracy of permeability evaluation by RF.

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张亚男,张冲,孙康,杨旺旺,汪明锐.混合模拟退火遗传和随机森林构建复杂碳酸盐岩储层渗透率测井解释模型[J].油气地质与采收率,2022,29(1):53~61

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  • 在线发布日期: 2022-03-30