Novel Method for Predicting Mercury Injection Capillary Pressure Curves of Tight Sandstone Reservoirs Using NMR T2 Distributions,Energy & Fuels

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Novel Method for Predicting Mercury Injection Capillary Pressure Curves of Tight Sandstone Reservoirs Using NMR T2 Distributions
Energy & Fuels ( IF 5.2 ) Pub Date : 2021-09-14 , DOI: 10.1021/acs.energyfuels.1c02146
Bohan Wu _{1,

2} , Ranhong Xie _{1,

2} , Mi Liu ₃ , Guowen Jin _{1,

2} , Chenyu Xu _{1,

2} , Jilong Liu _{1,

2}

Affiliation

In this research, mercury injection capillary pressure (MICP) experiments and nuclear magnetic resonance (NMR) experiments were carried out on 19 reservoir core samples. Based on the experimental results, the relationships between the capillary pressure (P_c) and NMR transverse relaxation time (T₂) were established. A novel classified piecewise multi-parameter power function transformation (CPMPFT) method is proposed to predict the MICP curves using the NMR T₂ distributions and is described as follows. First, the multi-parameter power function transformation model is established, after which the reservoir is classified and the appropriate pore segmentation point is selected. Then, for each type of reservoir, the coefficients of the transformation model for large and small pores are calculated based on the simulated annealing and genetic algorithm combined with non-linear programming, and the models for predicting the MICP curves are ultimately obtained. Using these models, the MICP curves can be predicted continuously based on the NMR T₂ distribution, based on which the pore structure parameters, including the displacement pressure (P_d), median pressure (P_c50), and so forth, can be precisely extracted. 19 core samples and well data were processed using the proposed method, and the predicted results were compared with the measured results; the results were found to match well, thereby proving the reliability of the proposed method.

中文翻译：

使用 NMR T2 分布预测致密砂岩储层注汞毛细管压力曲线的新方法

本研究对 19 个储层岩心样品进行了压汞毛细管压力 (MICP) 实验和核磁共振 (NMR) 实验。根据实验结果，建立了毛细管压力（P _c）与核磁共振横向弛豫时间（T ₂）之间的关系。提出了一种新的分类分段多参数幂函数变换 (CPMPFT) 方法来使用 NMR T ₂预测 MICP 曲线分布，描述如下。首先建立多参数幂函数变换模型，对储层进行分类，选择合适的孔隙分割点。然后，针对每种类型的储层，基于模拟退火和遗传算法结合非线性规划计算大小孔隙转换模型的系数，最终得到MICP曲线预测模型。使用这些模型，可以根据核磁共振T ₂分布连续预测 MICP 曲线，在此基础上孔隙结构参数，包括位移压力 ( P _d )、中值压力 ( P _c50) 等，可以精确提取。使用该方法对19个岩心样品和井数据进行了处理，并将预测结果与实测结果进行了比较；发现结果匹配良好，从而证明了所提出方法的可靠性。

更新日期：2021-10-07

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