Reservoir modelling based on 3D high-resolution seismic data

Summary: Three-dimensional (3D) reservoir models are best created with a combination of well logs and 3D seismic data. However, the effective integration of those results in the reservoir modeling was not easy due to limited seismic resolution. With the increasing quality of seismic data and widely application of new methods, High-resolution seismic stochastic inversion volume was used as a direct input to reduce the uncertainty of the reservoir model in this paper. Through the past years study, workflows were developed which use the high resolution seismic stochastic inversion as a direct input for reservoir modeling. The workflows mainly include three steps. The first step is target processing. Wavelet transform was applied to achieve noise elimination and resolution improvement. Base on the High-resolution seismic data, the second step is seismic stochastic inversion. After the process of time-depth conversion, the high resolution 3D data from seismic stochastic seismic inversion and well logs data were used to reservoir modeling as a direct input. Experiment results show that noise elimination and resolution improvement achieved by using wavelet transform brings desirable convenience, high efficiency and good fidelity. The seismic response of sand reservoir in the high resolution seismic section becomes clearer than the seismic data before target processing, and matched with the well log interpretation. As the seismic stochastic inversion process is controlled not only by the acoustic impedance features, variographic model, and histogram, but also by high-resolution seismic data, the possible number of solutions is reduced, thus decreasing the nonuniqueness of the solution. The seismic stochastic inversion results were multiple 3D volumes with the same horizontal resolution of the seismic and with the vertical resolution, which is matched with the well data. The reservoir model base on the high-resolution 3D data from seismic stochastic inversion and well logs data will reduce the uncertainty greatly, especially in the area Where reservoir exhibits strong heterogeneity. A postmortem is presented showing a successful well that were better explained by this model result based on data existing before the well were drilled.