Constructing high-resolution stratigraphic frameworks by the application of signal analysis techniques: Example of Balbuena IV sequence, Yacoraite formation, Salta Basin

Abstract

One of the most essential and valuable applications of high-resolution sequence stratigraphy is reservoir zonation and characterization. Establishing a hierarchical chronostratigraphic framework comprising sequences at multiple scales is crucial to identify main reservoir heterogeneities that compartmentalize the fluid flow in the subsurface. In this context, this paper proposes a workflow that integrates different signal analysis techniques to identify sequences of multiple hierarchies from a series of stratigraphic data. The Continuous Wavelet Transform (CWT), Detrend Error Log (DTEL), and Integrated Detrend Error Log (INDTEL) techniques are utilized to process gamma-ray data obtained from outcrops of the Yacoraite Formation, which are conventionally employed as analogs for reservoir characterization. Our results suggest that CWT fits better with higher frequency cycles, while INDTEL shows a good fit with medium frequency cycles. In addition to these techniques, Hidden Markov Models were also applied, predicting T-R cycles as hidden states from the facies transition matrix and possible occurrence of missed beats. All these methods were scripted in the Python programming language, enabling the generation of fast and interactive outputs. This approach brings parameters that can guide the construction of stratigraphic models by automated processes.