Abstract

High-Resolution Sequence Stratigraphy is fundamental for attaining a refined and representative geological 3D model for oil and gas reservoirs. For its application on turbidite systems it is essential to have a robust facies and facies association framework, recognizing architectural elements and the cyclicity in different frequencies on the stacking pattern. This study is widely based on a multi-scale approach (4th and 5th orders), integrating satellite image, outcrop descriptions and microscopic data to achieve a predictive depositional model through time. The Jurassic Los Molles Formation, Neuquén Basin, was chosen as a case study due to its outcrop quality of offshore shelf (Carreri section) and slope/basin plain settings (La Jardinera section). This enabled a rich comparison between facies associations, architectural elements, stacking patterns and cyclicity on turbidite systems of different palaeo physiographic settings. The Carreri offshore shelf turbidites display higher confinement with predominance of tractive structures and coarser grain-sizes as opposed to deepwater deposits. Their base level variations are recorded by the stacking of turbidite channel-overbank systems to delta front deposits and tidal bars that culminate important regressions. In contrast, in La Jardinera slope and basin plain succession, the falling stage system tract of turbidite sequences is characterized by amalgamated composed lobes and wide composed channels with coarser grain-sizes and erosive contacts. The lowstand system tract is represented by less amalgamated lobes and interlobes, suggesting the system backstepping until the muddy transgressive system tract. The workflow of this study allows a better understanding of the evolution of a sedimentary system, correlating this to important stratigraphic surfaces that can represent fluid flow barriers and reservoir heterogeneities that could impact on production data. High resolution sequence stratigraphy, especially in turbidite systems, is key for more accurate reserve estimates and for developing predictive and economic exploration and production strategies in the petroleum industry.

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