A Novel Model of Predicting Archie＇s Cementation Factor from Nuclear Magnetic Resonance （NMR） Logs in Low Permeability Reservoirs

• Published in: 地球科学学刊：英文版 2014 (1), p.183-188
• Main Author: Liang Wang Zhiqiang Mao Yujiang Shi Qin＇e Tao Yumei Cheng Yong Song
• Format: Article
• Language: English
• Subjects: NMR测井; 低渗透储层; 岩石电阻率; 日志; 核磁共振T2谱; 油藏预测; 胶结指数; 预测模型
• ISSN: 1674-487X; 1867-111X

The resistivity experimental measurements of core samples drilled from low permeability reservoirs of Ordos Basin, Northwest China, illustrate that the cementation factors are not agminate, but vary from 1.335 to 1.749. This leads to a challenge for the estimation of water and hydrocarbon sa- turation. Based on the analysis of Purcell equation and assumption that rock resistivity is determined by the parallel connection of numerous capillary resistances, a theoretical expression of cementation factor in terms of porosity and permeability is established. Then, cementation factor can be calculated if the parameters of porosity and permeability are determined. In the field application, porosity can be easily obtained by conventional logs. However, it is a tough challenge to estimate permeability due to the strong heterogeneity of low permeability reservoirs. Thus, the Schlumberger Doll Research （SDR） model derived from NMR logs has been proposed to estimate permeability. Based on the analysis of the theoretical expressions of cementation factor and SDR model, a novel cementation factor prediction model, which is relevant to porosity and logarithmic mean of NMR T2 spectrum （T21m）, is derived. The advantage of this model is that all the input information can be acquired from NMR logs accurately. In order to confirm the credibility of the novel model, the resistivity and corresponding laboratory NMR measurements of 27 core samples are conducted. The credibility of the model is confirmed by compar- ing the predicted cementation factors with the core analyzed results. The absolute errors for all core samples are lower than 0.071. Once this model is extended to field application, the accuracy of water and hydrocarbon saturation estimation will be significantly improved.