Fracture Prediction in a Carbonate Reservoir Based on Logging-Seismic Data

Carbonate reservoir formations are characterized by the development of fractures and strong heterogeneity.Under such circumstances, singular logging or seismic techniques have limitations in the identification of fractures.In the concerned study, a method involving a combination of logging and seismic data was proposed for predicting fracture development in carbonate reservoir formations.First of all, the rough set theory could be combined with development of fractures to transform weight coefficient problem into the attribute evaluation in rough set.In this way, fractures identified by using logging data could be calibrated by using drilling and coring data.Second, based on azimuthal anisotropy of pre-stacking seismic data, the fracture development, dominant azimuth and fracture growth density could be predicted;Finally, the matching of logging and seismic scale was completed through drilling-seismic calibration.In addition, the functional relationship between the logging fracture extension recognition results and pre-stack seismic anisotropy detection intensity could be matched to highlight extent of the seismic data and grades of fractures.Field data were used to predict fracture development in carbonate reservoir formations through a combination of logging-seismic data.Research results showed the proposed method could effectively enhance the reliability of prediction.In addition, fractures of various scales in both horizontal and vertical directions could be identified effectively.Relevant research might provide valuable references for prediction of fracture development in carbonate reservoir formations.