利用图论多分辨率聚类方法构建核磁可变T2截止值预测模型
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P631

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国家自然科学基金项目(面上项目,重点项目,重大项目)


A graph theory multi-resolution clustering method was used to construct the NMR variable T2 cutoff Prediction model
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    准确求取T2截止值对核磁共振资料在储层评价中的应用至关重要,传统T2截止值获取方法受到区域性、储层非均质性等影响明显,实际使用中往往误差较大。本文基于岩心核磁实验,分析物性参数、核磁共振T2谱几何均值等参数与T2截止值之间的关系。考虑到储层非均质的影响,提出采用图论多分辨率聚类方法,利用邻近指数和核心代表指数划分测井相,结合铸体薄片等资料建立测井相与岩相的对应关系,建立基于测井相约束的可变T2截止值预测模型。在渤海盆地X油田实际应用中表明,本文提出的模型计算的T2截止值与岩心核磁实验T2截止值测量值误差较小,较传统方法优势明显,进一步提高了利用核磁共振资料计算储层参数的准确性与适用性。

    Abstract:

    Obtaining accurate T2 cutoff value is very important for reservoir evaluation using NMR data. Traditional T2 cutoff value acquisition method is obviously affected by regional actual situation and reservoir heterogeneity, and often has large error in practice. Based on core NMR experiments, this paper analyzes the relationship between physical parameters, Geometric mean of T2 spectrum in NMR and T2 cutoff values. Considering the influence of reservoir heterogeneity, a graph theory multi-resolution clustering method is proposed to classify logging facies by using neighboring index (NI) and kernel representative index (KRI). The corresponding relationship between logging facies and lithofacies is established based on casting thin section and other data, and a prediction model of variable T2 cutoff value based on logging facies constraint is established. The practical application of X oilfield in Bohai Basin shows that the error between the T2 cutoff value calculated by the proposed model and the T2 cutoff value measured by core NMR experiment is small, which has obvious advantages over the traditional method, and further improves the accuracy and applicability of using NMR data to calculate reservoir parameters.

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年泽润,张超谟,张冲,等. 利用图论多分辨率聚类方法构建核磁可变T2截止值预测模型[J]. 科学技术与工程, 2022, 22(25): 10867-10875.
Nian Zerun, Zhang Chaomo, Zhang Chong, et al. A graph theory multi-resolution clustering method was used to construct the NMR variable T2 cutoff Prediction model[J]. Science Technology and Engineering,2022,22(25):10867-10875.

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  • 收稿日期:2021-11-16
  • 最后修改日期:2022-07-28
  • 录用日期:2022-05-05
  • 在线发布日期: 2022-09-29
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