Assessing Uncertainty in Stratigraphic Correlation: A Stochastic Method Based on Dynamic Time Warping

Jonathan Edwards and Florent Lallier and Guillaume Caumon and C├ędric Carpentier. ( 2014 )
in: Second EAGE Integrated Reservoir Modelling Conference, EAGE

Abstract

We propose a method to manage uncertainties about the layering of 3D reservoir models, using stochastic correlations of sedimentary units identified along wells, according to the sequence stratigraphy paradigm. A stratigraphic model represents the architecture of the stratigraphic succession of an area. Sequence stratigraphy is a common paradigm in reservoir studies to interpret and correlate local high resolution observations (outcrops, well logs and core samples) and more exhaustive but lower resolution data such as 3D seismic. The incompleteness of these data, their quantity and their varying quality, added to the fact that the processes that control the geometry and the conformability of the sequences are complex and poorly known, lead to uncertainties. The proposed method aims at building stratigraphic models honoring 1D interpretations along wells together with conceptual sequence stratigraphic rules formulated quantitatively as correlation costs. The algorithm chosen is a modified version of the Dynamic Time Warping algorithm. More than finding the best correlation using a set of rules, it handles different orders of sequences, takes in account the conformability of the horizons, and its output is a set of different possible correlations, allowing for generating alternative stratigraphic layerings. This methodology is demonstrated on the Teapot Reservoir, Wyoming.

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BibTeX Reference

@inproceedings{edwards:hal-04068794,
 abstract = {We propose a method to manage uncertainties about the layering of 3D reservoir models, using stochastic correlations of sedimentary units identified along wells, according to the sequence stratigraphy paradigm. A stratigraphic model represents the architecture of the stratigraphic succession of an area. Sequence stratigraphy is a common paradigm in reservoir studies to interpret and correlate local high resolution observations (outcrops, well logs and core samples) and more exhaustive but lower resolution data such as 3D seismic. The incompleteness of these data, their quantity and their varying quality, added to the fact that the processes that control the geometry and the conformability of the sequences are complex and poorly known, lead to uncertainties. The proposed method aims at building stratigraphic models honoring 1D interpretations along wells together with conceptual sequence stratigraphic rules formulated quantitatively as correlation costs. The algorithm chosen is a modified version of the Dynamic Time Warping algorithm. More than finding the best correlation using a set of rules, it handles different orders of sequences, takes in account the conformability of the horizons, and its output is a set of different possible correlations, allowing for generating alternative stratigraphic layerings. This methodology is demonstrated on the Teapot Reservoir, Wyoming.},
 address = {Dubai, United Arab Emirates},
 author = {Edwards, Jonathan and Lallier, Florent and Caumon, Guillaume and Carpentier, C{\'e}dric},
 booktitle = {{Second EAGE Integrated Reservoir Modelling Conference}},
 doi = {10.3997/2214-4609.20147469},
 hal_id = {hal-04068794},
 hal_version = {v1},
 month = {November},
 organization = {{EAGE}},
 title = {{Assessing Uncertainty in Stratigraphic Correlation: A Stochastic Method Based on Dynamic Time Warping}},
 url = {https://hal.univ-lorraine.fr/hal-04068794},
 year = {2014}
}