Three-dimensional seismic-based lithology prediction using impedance inversion and neural networks application: Case-study from the Mannville Group in East-Central Alberta, Canada.

摘要

The Lower Cretaceous Mannville Group in East-Central Alberta is one of the main targets for hydrocarbon exploration in Western Canada, typically associated with stratigraphic controls. The depositional environment is diverse, ranging from fluvio-continental to shallow marine. Mannville Group strata overlie a major unconformity that separates them from predominantly carbonate rocks of the Paleozoic (Devonian). This study sought to integrate well and 3-D seismic data to create a stratigraphic architecture for the zone of interest. Seismic inversion was applied to the data and proved to be an excellent tool for mapping the unconformity, clearly distinguishing between the clastic rocks above, and the units below. The inversion result was included as part of a seismic attribute study applied to the dataset. Stepwise regression and validation testing indicated that a combination of six attributes was found to provide the best set able to predict log-derived physical properties related to lithology (gamma-ray) using the seismic data. This result was used to train a neural network, and finally, a pseudo-lithology volume able to estimate the distribution of lithology within the Mannville Group over the whole 3-D survey was generated. New stratigraphic features that were non-apparent in the original amplitude seismic version were discovered using the inversion and the pseudo-lithology volumes. The results of this study show how comparing the three different 3-D seismic versions can be useful for understanding the stratigraphic complexity of the Mannville. The extensive methodology approach presented herein can be used for analog purposes in Western Canada as well as in any other geological setting.