Automatic gas chimney detection from 3D seismic reflection data using a single amplitude attribute

Automatic gas chimney classification methods use selected seismic attributes as training inputs for machine -learning algorithms. Contrary to the common belief that a single attribute is insufficient, the scope of this paper is to introduce an algorithm for gas chimney detection and extraction based on the amplitude attribute. The method is applied to a 3D seismic cube and involves the use of a feature extraction process (maximum overlap discrete wavelet transform -MODWT), before applying a standard machine-learning algorithm (bidi-rectional long short-term memory -BiLSTM). The MODWT is used to extract four unique features of each trace, which are then used with the original trace for training. The idea is to introduce a new feature-extraction step that enhances the accuracy of a single attribute. When tested on the F3 Block seismic cube, the integration of this process with the BiLSTM deep-learning algorithm achieved a balanced accuracy of 93% in gas chimneys iden-tification. Results calibration by an experienced seismic interpreter also show a good match between machine -learning algorithm and manual interpretation. Therefore, our results successfully demonstrate that gas chimneys can be automatically detected and extracted from 3D seismic data using a single attribute. The workflow pre-sented here is applicable worldwide for studies of different economic and environmental purposes (e.g., natural resources of petroleum exploration and gas hydrate, subsurface storage characterization and geohazards identification).