A prediction method of borehole stability based on seismic attribute technology

Borehole instability in drilling engineering can bring about serious problems of drilling quality and safety. Based on the close relationships between seismic and well log information, the prediction method of borehole stability is presented to effectively control borehole instability. Conventional and nonlinear seismic attributes are extracted from borehole-side seismic traces of impending drilling well and drilled offset well respectively. Then the optimal attributes combinations sensitive to log properties are selected by using genetic algorithm and wavelet neural network technology together. A series of mapping models which reflect the nonlinear relationships between seismic attributes and acoustic and density log data of various formation intervals in drilled well are constructed through neural network modeling. With analysis of cutting logging data, seismic attributes of the formation under bit and corresponding mapping model can be used to predict acoustic and density log curves of this formation. Based on the predicted log data, log interpretation method, analysis technology of in-situ stress and mechanics model of borehole stability are employed to calculate in-situ stress, pore pressure, collapse pressure and fracture pressure, thus the safe drilling fluid density window which can keep borehole stable is determined. Prediction precision and real-time operation ability of the proposed method are satisfying, which have been proved in practical application in TR oil field.