The root mean square(RMS) difference of time-lapse seismic amplitudes is routinely used to identify the substituted fluid type in a reservoir during oil field production and recovery. By a time-lapse seismic method, we study the effects of fluid substitution in a physical model, which is an analogy of the three-dimensional inhomogeneous reservoir. For a weak inhomogeneous medium, gas/oil substitution results in positive anomalies in the reservoir layers, and negative anomalies below the bottom of the reservoir layers; while water/oil substitution causes only weak variations in the reservoir layers, but positive anomalies below the bottom of the reservoir layers. For the strong inhomogeneous medium, no matter what kind of fluid substitution(gas/oil or water/oil), there are significant anomalies in seismic amplitude difference attributes both in and below the reservoir layers. Therefore, for weak inhomogeneous media, such as tight sandstone or thin interbedded layers, the RMS amplitude difference attributes can be used to monitor fluid changes and predict the drilling direction; for inhomogeneous medium with karst carves or fractures, it is difficult to accurately determine the distribution of fluids with the RMS amplitude difference attributes.
The first generation coherence algorithm(the C1 algorithm) that calculates the coherence of seismic data in-line and cross-line was developed using statistical cross-correlation theory, and it has the limitation that the technique can only be applied to horizons. Based on the texture technique, the texture coherence algorithm uses seismic information in different directions and differences among multiple traces. It can not only calculate seismic coherence in in-line and cross-line directions but also in all other directions. In this study, we suggested first an optimization method and a criterion for constructing the gray level co-occurrence matrix of the seismic texture coherence algorithm. Then the co-occurrence matrix was prepared to evaluate differences among multiple traces. Compared with the C1 algorithm, the seismic texture coherence algorithm suggested in this paper is better than the C1 in its information extraction and application. Furthermore, it implements the multi-direction information fusion and it, also has the advantage of simplicity and effectiveness, and improves the resolution of the seismic profile. Application of the method to field data shows that the texture coherence attribute is superior to that of C1 and that it has merits in identification of faults and channels.
