Presenting an integrated strategy for porosity mapping in a genetic-based seismic inversion framework in a heterogeneous reservoir

Seismic reservoir characterization is a state-of-the-art procedure in using various sources of data. Generally, seismic data, due to their low resolution, are randomly used in the final steps of reservoir characterization. However, extensive coverage of 3D seismic data, compared to well data, makes it possible to be applicable for the distribution of characters through the whole reservoir. In this regard, seismic data should be inverted to illustrate the desired characters throughout the media. Conventionally, seismic inversion uses well logs that have defects in its derivation steps, such as wavelet extraction and its propagation through media. The proposed strategy to resolve such deficiencies is the genetic inversion. However, genetic inversion has its own deficiency in accuracy. In this study, we propose an integrated strategy for using various sources of data in an iterative manner for resolving this obstacle. The proposed strategy uses a combined related attribute to evaluate initial acoustic impedance inverted model by genetic inversion. The model then would be updated to satisfy well data. The proposed strategy was applied to a heterogeneous reservoir from the southwest of Iran. Three seismic attributes were integrated to produce a unique attribute for initial model evaluation. The final model was then evaluated by well data. Results were also compared with the conventional method of seismic inversion. The result of the proposed strategy in the genetic inversion depicted improvement in the final acoustic impedance and the porosity distribution model.