Affiliation:
1. Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company
2. Surignan Operating Company, PetroChina Changqing Oilfield Company
Abstract
Abstract
Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum. The relationship between the MICP curve and the corresponding inverse cumulative curve of porosity spectra was analyzed, and the model of piecewise power function, which can be used to transform the porosity spectrum as pseudo capillary pressure (Pc) curve, was established. By using this model, consecutive pseudoPc curves can be constructed in the intervals with which XRMI data was acquired, and the corresponding pore structure evaluation parameters, such as the average pore throat radius, the maximum pore throat radius, the threshold pressure, and so on, can also be predicted. Meanwhile, a permeability prediction model based on the Swanson parameter, also established. By combining with the constructed consecutive pseudoPc curves, the pore structure evaluation parameters and permeabilities, several hydrocarbon production potential formations were identified, and this was verified by the drill stem test (DST) data.