Permeability Evaluation from Logs based on Classification and Optimization of Flow Unit in Tight Sandstone
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摘要:
致密砂岩储层非均质性强，常规测井解释模型没有考虑储层纵向上渗流特征的差异性，导致渗透率解释精度低。为此，采用储层流动单元描述致密砂岩储层非均质特征，建立了具有不同渗流单元的渗透率解释模型，以提高渗透率预测精度。首先，结合岩心实验流动单元指数频数分布与累计分布频率，建立流动单元分类标准，并优选流动单元分类数目，分类构建渗透率模型；然后，引入深度神经网络，结合常规测井和核磁测井数据，预测流动单元指数；最后，基于分类渗透率解释模型，计算储层渗透率。珠江口盆地惠州凹陷古近系恩平组应用该渗透率计算方法进行计算，流动单元分为5类最佳，测井尺度的流动单元识别分类与沉积相具有较好的一致性，渗透率计算准确度相比核磁模型明显提高。研究结果为深层致密砂岩储层渗透率评价提供了新的计算方法。
Abstract:Conventional log interpretation models usually overlook the longitudinal flow properties due to the heterogeneity of tight sandstone reservoirs, leading to low accuracy in permeability interpretation. Therefore, the flow units (FU) are utilized to describe the heterogeneity characteristics of tight sandstone reservoirs. Constructing permeability models of different FU are expected to improve the evaluation accuracy of permeability. In this study, the flow unit classification standard is established by combining the frequency distribution histogram and cumulative probability plot of experimental Flow Zone Indicator (IFZ), and the optimal number of flow unit is selected to establish multiple permeability models. Additionally, the deep neural network (DNN) is introduced to predict IFZ by combining conventional logging and nuclear magnetic resonance (NMR) logging data. Finally, well permeability is calculated based on multiple permeability interpretation models. This permeability calculation method is applied to Paleogene Enping Formation in HuiZhou depression, Pearl River Mouth Basin, and five FU types are selected to optimal classification. The application result shows that the predicted FU types are well aligned with sedimentary facies and the accuracy of permeability calculations has significantly improved compared to NMR model. Overall, the permeability calculation method based on classification and optimization of FU provides a new insight for accurate evaluation of logging permeability in deep tight sandstone reservoirs.
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Keywords:
- tight sandstone /
- permeability evaluation /
- flow unit /
- deep learning
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图 2 建立流动单元分类标准的基本方法
Figure 2. The methods for establishing classification criteria of flow units
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图 4 基于流动单元分类和优选的储层渗透率计算流程
Figure 4. Permeability calculation workflow based on classification and optimization of flow unit
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图 6 测井参数对流动单元指数预测精度的影响
Figure 6. Influence of logging parameters on the prediction accuracy of IFZ
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图 8 不同流动单元分类及其对应的渗透率解释模型
Figure 8. Classification of different fu and their corresponding permeability interpretation models
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图 9 不同流动单元分类的渗透率计算结果
Figure 9. Calculation results of permeability for different FU classifications
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图 10 惠州油田HZ–A井渗透率计算实例
Figure 10. Example of permeability calculation of Well HZ–A in Huizhou Oilfield
表 1 5类流动单元类型的储层特征
Table 1 Reservoir properties of flow units with five types
流动单元分类 沉积微相 流动单元指数/μm 岩性 孔隙度，% 渗透率/mD Ⅰ类 分流间湾 IFZ≤0.45 泥质粉砂岩、中–细砂岩为主 5.4～15.0（9.4） 0.01～0.96（0.08） Ⅱ类 河口坝 0.45 < IFZ≤1.42 中–细砂，少量粗砂和含砾中砂 5.0～16.1（9.8） 0.07～11.00 （0.75） Ⅲ类 河口坝, 分流河道 1.42< IFZ≤3.00 中–细砂为主，含砾粗砂次之 6.2～16.2（10.7） 0.56～48.00 （6.76） Ⅳ类 分流河道 3.00< IFZ≤8.00 中–粗砂为主，含砾粗砂次之 7.0～15.2（12.1） 2.9～379.0（70.0） Ⅴ类 分流河道 IFZ >8.00 粗砂和含砾粗砂 9.0～17.6（14.2） 101～ 3611 （758）注：孔隙度及渗透率数值后（）内数值为平均值。 
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