Fractures are effective storage spaces and important seepage channels for tight reservoirs and are thus critical to their exploration and development. At present, single-well fracture identification is attained mainly via imaging logging, array acoustic logging and conventional logging. In continental reservoirs, the diversity of the main factors controlling fracture development leads to strong randomness and heterogeneity. As a result, fracture identification must overcome problems such as weak effective and considerable ambiguous logging response signals. Accurately identifying fractures is a critical issue in studies of tight sandstone reservoirs. In this work, data from 13 cores and statistical data from fracture observations made during the imaging logging of four noncored wells in the study area were collected. Accordingly, a fracture identification database containing different types of conventional logging curves and information on layers, fracture development and well location was established. Under the constraints of stratigraphy and locations of fracture development and properties of fractures determined by core or imaging log observations, a mathematical model with the highest percentage of identified fractures was optimized to identify tectonic fractures on the basis of conventional logging. Compared with imaging logging, the rate of fracture recognition of the proposed method was 77.29%, which was significantly better than the rates of the range/standard deviation (R/S) method (66.7%) and the trigonometric projection method (64.6%). Therefore, the method had strong practicability, reliability and reference value for increasing the fracture recognition rate, analyzing tectonic mechanisms and performing quantitative predictions.
Liu et al. (Fri,) studied this question.