Reservoir lithology classification based on seismic inversion results by Hidden Markov Models: Applying prior geological information. (May 2018) - Feng, Runhai; Luthi, Stefan M.; Gisolf, Dries; Angerer, Erika
Abstract: Hidden Markov Models (HMMs) have been applied to predict reservoir lithologies using seismic inversion results as inputs. This approach takes into account the conditional probabilities between different lithologies, i.e. the vertical transitions in sedimentary sequences. These properties are used as prior geological information. In order to relate the seismic inversion results to the true well-log data, HMMs need to be trained based on the Expectation-Maximization theory. Application of the resulting model on a synthetic example from the Book Cliffs (Utah, USA) showed that most lithologies are classified correctly, even for some thin layers. A comparison with point-wise methods in which data samples are treated independently from each other, such as k -means and fuzzy logic classifiers, leads to the conclusion that the spatial correlation in HMMs allows better lithological predictions because the prior information accounts for the geological depositional processes. A real case study with data from the Vienna Basin (Austria) is performed, in which lithologies in a 3D cube are obtained based on properties from seismic inversions, via trained HMMs. While the vertical sequences are shown to be reasonably well predicted, the horizontal continuities are not. This indicates that the future research should focus on the lateral geological relationships. Highlights: An effort to exact the reservoir parameters in terms of lithologies has been demonstrated. Instead of using well-logging data as inputs, the inversion results of seismic data have been proposed. The data samples in the subsurface are treated dependent with each other by the Markov chain sequence. This is only 1D result, even though 3D images have been shown. As a future research, the dependency in the lateral direction should be considered.