Abstract
Accurate permeability characterization is essential for evaluating shale oil reservoirs quality. Chang 7 shale oil reservoirs, with diverse lithologies, low porosities and permeabilities, complex pore structures, and strong heterogeneity, pose challenges for traditional permeability prediction methods. This paper proposes an improved method that integrates lithology and hydraulic flow units (HFUs) with conventional logs for enhanced permeability prediction. The reservoir characteristics of the Chang 7 member in the Sai 392 area were analyzed, investigating relationships among lithology, porosity, pore structure parameters, and permeability. Lithology was identified by combining imaging logging and lithologic reconstruction curves (I(lith)), with I(lith) value boundaries of - 0.5, 1.5, and 3 for fine sandstone, argillaceous siltstone, silty mudstone, and mudstone, respectively. A back-propagation neural network predicted the flow zone index using lithological evaluation parameters and logging data, enabling HFU dentification and permeability model establishment. In this case study, the improved method was successfully implemented to accurately predict the permeability of the Chang 7 member shale oil reservoirs. Almost all data in the crossplot of the predicted versus measured permeability are within ± 0.17 uncertainty boundaries, with a root mean square error lower than 0.051. The results demonstrate that the improved method can effectively and accurately predict the permeability of the Chang 7 member shale oil reservoirs in the Sai 392 area.