The relative permeability (K-r) measurement of tight sandstones is challenging due to its low porosity, low permeability, and complex pore structure. Nuclear magnetic resonance (NMR) technology has the advantages of being fast, nondestructive, and noninvasive while also continuously evaluating tight reservoirs. Based on NMR technology, it is of great significance to establish an effective and reliable relative permeability prediction model to solve practical problems in oil fields. In this paper, a method for predicting gas-water relative permeability in tight sandstone reservoirs is proposed based on NMR transverse relaxation time (T-2) distribution. The gas-water relative permeability measurements are performed for tight sandstone reservoirs in the Central Sichuan Basin, China. On the basis of the analysis of the gas-water permeability features in the study area, the reservoir characteristics are clarified. Based on the NMR theories, two T-2-K-r prediction models (model 1 and optimal model 2) are derived and established, and the model performance is analyzed using experimental data and existing models (Purcell and Brooks-Corey). Finally, the optimal model is used to process NMR logging data, obtain continuous relative permeability curves, and perform a productivity prediction. The effectiveness and applicability of the method are verified using relative permeability experiments and the oil testing data. The proposed method can provide effective guidance for the prediction of relative permeability curves and productivity evaluation of tight sandstone reservoirs.

致密砂岩储层孔隙结构是影响储层渗流能力和产能的重要因素.以四川盆地沙溪庙组致密砂岩储层为研究对象,基于高压压汞实验和双峰韦伯分布函数,建立致密砂岩储层孔隙结构表征模型;拟合致密砂岩储层毛细管压力曲线,分析双峰韦伯分布参数(权重、均值半径和标准差)与物性、孔隙结构参数的关系,建立沙溪庙组致密砂岩储层分类标准,划分研究区致密砂岩储层类型.结果表明:双峰韦伯分布函数可以有效表征致密砂岩储层毛细管压力曲线,拟合结果与岩心实验数据吻合较好.双峰韦伯分布参数能够反映储层的物性和孔隙结构特征,研究区大孔的孔隙度、大/小孔的孔喉均值半径可以定量表征储层孔隙结构,根据物性、孔隙结构等参数将储层划分为Ⅰ、Ⅱ、Ⅲ 类,测井资料划分结果与现场试气结果吻合较好.该分类方法为致密砂岩储层的勘探开发及储层评价提供技术支撑.

The components and pore structure of shale are complex due to the heterogeneous distribution of organic matter and the complex distributions of the minerals. The digital core, possessing the advantages of being economical and reusable, can be widely used to directly characterize the three dimensional (3D) micro-pore structure and to numerically simulate its physical properties. During construction of a digital shale core, it is a challenge to solve the multicomponent segmentation for the digital shale core, the contradiction between the sample size and image resolution, and the identification of the pore types in the 3D pore space. Therefore, an automatic workflow based on the gray gradient-maximum entropy-3D morphology was developed. The gray gradient-maximum entropy algorithm was used to segment each sub-image of focused ion beam scanning electron microscope images to generate segmented images. On this basis, the pore size distribution was optimized via 3D morphological erosion. Based on the concept of pore clusters, the organic and inorganic pores were identified using the 3D morphological method for the first time. The construction of a multicomponent digital shale core was realized. The 3D micro-pore structure of the organic and inorganic pores was characterized by pore connectivity, heterogeneity, and pore size distribution. The accuracy of the proposed method was verified using low-temperature N2 adsorption experiment data. The results of this study provide new insight into the multicomponent digital shale core construction and lay the foundation for the characterization of the petrophysical properties and micro-/nano-scale fluid flow simulations of shale.

The classification of tight sandstone reservoirs is of significance for hydrocarbon exploration and production. Different from conventional reservoirs, tight sandstone reservoirs are characterized by a complex pore structure and strong heterogeneity. Tight sandstone reservoir classification may not be reliable with conventional reservoir classification methods. In this paper, a classification method of tight sandstone reservoirs was proposed on the basis of the nuclear magnetic resonance (NMR) transverse relaxation (T-2) distribution, and it was employed for reservoir classification on the Shaximiao Formation in central Sichuan, China. As a result of the complex pore structure of a tight sandstone reservoir, the NMR T-2 distribution is characterized by the presence of multiple peaks. Therefore, the three-peak Gaussian function was used to fit the T-2 distribution and obtain the characteristic parameters. On the basis of high-pressure mercury intrusion (HPMI) experiments, the correlation between the characteristic parameters of the NMR T-2 distribution and pore structure parameters and petrophysical properties was analyzed, and then the optimal characteristic parameters of the NMR T-2 distribution were selected according to the results of correlation analysis and used to establish the pore structure index. In combination of petrophysical properties with the pore structure index, the model for classification of tight sandstone reservoirs was established by the naive Bayesian method based on hierarchical clustering, and the accuracy of the model was verified by k-fold cross-validation. Finally, the effectiveness of the proposed method was verified using the actual NMR logging data in conjunction with the oil test data. The results showed that the method for classification of tight sandstone reservoirs based on the NMR T(2 )distribution can provide effective guidance for production capacity evaluation.

Irreducible water saturation, affecting the productivity of a reservoir, is one of the key parameters for a tight sandstone reservoir. Nuclear magnetic resonance (NMR) logging, detecting the relaxation information on pore fluid with hydrogen nuclei in formation, can be used to distinguish irreducible and movable fluids as a result of their different relaxation information. The irreducible water saturation can be determined by the T-2 cutoff method in the conventional reservoir. However, a tight sandstone reservoir possesses a complex pore structure, and relaxation signals of different pore fluids may overlap in the T-2 distribution. The T-2 cutoff method to determine irreducible water saturation may be a challenge in tight sandstone reservoirs. In this study, a novel T-2 distribution-based method, with the film model assumption, was proposed to determine the irreducible water saturation in tight sandstone reservoirs. The T-2 distribution was transformed by a continuous wavelet transform to obtain the two-dimensional matrix of wavelet coefficients, and the position and shape parameters of the spectral peak at different scales in wavelet space were determined. These parameters were used to construct the Gaussian distribution functions (GDFs) at different scales. The T-2 distribution was decomposed on the basis of the constructed GDFs, and then the optimal decomposition was selected. The irreducible water saturation is determined by the optimal T-2 distribution decomposed. The effectiveness and practicability of the proposed method were validated by numerical simulation, core experimental, and NMR logging data processing.

During oil and gas exploration, it is difficult to quantitatively evaluate fluid components and accurately calculate the saturation of different fluids because of the overlapping of fluid components on 2D NMR spectrum. In this paper, Blind Source Separation (BSS) is proposed to separate fluid components, which utilizes the statistical independence of fluid signals on 2D NMR spectrum. Fast Independent Component Analysis (FastICA) is employed for the inverted NMR spectrums in an entire logged interval to obtain the residual information to determine the number of fluid components. Based on the determined number of fluid components, Nonnegative matrix factorization (NMF) is used to obtain the features of fluid components on NMR spectrum and the region on 2D NMR spectrum is divided into different regions. The overlapping regions are classified by distance or distance and T1/T2 to obtain the modified NMR spectrum. Through T2-D and T1-T2 numerical simulation, the fluid saturations calculated by the proposed method and NMF are compared to verify the effectiveness of the proposed method. The results showed that the proposed method can be used to determine the number of fluid components effectively, and the calculated fluid saturations are more accurate than that obtained by NMF.

致密砂岩储层孔隙结构复杂,非均质性强,岩性识别困难.传统的基于特征曲线进行曲线重叠、构造参数或建立交会图识别岩性的方法依赖解释人员的知识和经验,而机器学习方法基于测井曲线和岩性类别的映射关系进行数理统计,无法直接观察岩性识别的过程.因此,提出利用主成分分析法来对致密砂岩岩性进行识别,选取对研究区岩性敏感的自然电位、补偿中子、密度、声波时差和核磁共振横向弛豫时间分布T2几何均值曲线作为主成分分析法的输入变量,提取累计贡献率为91％ 的主成分F1和F2建立交会图识别岩性.选取鄂尔多斯盆地姬塬地区的岩心分析资料和测井资料对主成分分析法的识别结果进行验证,结果表明:F1—F2交会图有效划分含砾粗砂岩、中砂岩、细砂岩和泥岩;对于砂岩的含油级别,交会图能有效划分荧光细砂岩、油迹细砂岩和油斑细砂岩.通过应用实例分析表明,主成分分析法对致密砂岩岩性的识别具有可行性.

The mercury injection capillary pressure (MICP) curves have been widely used for the evaluation of the pore structure. In practical application, the MICP curves are usually obtained by the mercury injection experiment, but the experimental measurement can not obtain the MICP curves of the reservoir continuously. Since the pore size distribution obtained from the nuclear magnetic resonance (NMR) transverse relaxation time (T2) distribution is related to the pore-throat size distribution obtained from the MICP curve, so in previous studies, the MICP curves were predicted based on the NMR T2 distribution. However, the NMR T2 distribution obtained by the inversion of the NMR echo data has uncertainty, which affects the prediction accuracy of the MICP curves. In this paper, a new method for predicting the MICP curves of sandstone based on NMR echo data is proposed for the first time. Multiple characteristic parameters of the NMR echo data are calculated. The relationship between the parameters and the mercury saturation (Shg) is established at each capillary pressure point to predict the MICP curves. And the parameters of the MICP curves and the physical parameters are selected to create a reservoir classification index. The results show that the method for predicting the MICP curves of sandstone based on NMR echo data has high prediction accuracy and strong stability, and the reservoir classification index provides an accurate classification of sandstone reservoirs.