1887

Abstract

Summary

Stratigraphie well correlations are nowadays performed deterministically and manually, based on the sequence stratigraphy concept. The major is the difficulty to fully identify the different factors that control the 3D strata architecture. From a single well dataset, the stratigraphic well correlation model construction could lead to a non unique answer.

Thanks to the development of Stratigraphic Forward Modeling (SFM) algorithms, one can now test the impact of the different controlling processes on resulting stratigraphic architecture.

We here propose a semi-automatic methodology that builds stratigraphic correlation models by learning from the results of a SFM, with a stochastic approach.

We introduce two objective functions that can be used to compare a SFM with well data: the facies similarity and the thickness variation similarity. We also show how they can be used to assess the consistency of manual stratigraphic well correlation and how they can be minimised leading to the automatic construction of stratigraphic well correlation and stratigraphic grids.

By doing so, our methodology uses the SFM capacity to integrate several interdependent controlling factors to generate several stratigraphic well correlation models constrained to well data corresponding to a single well dataset and thus to the uncertainties on a well-to-well correlation.

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/content/papers/10.3997/2214-4609.201700733
2017-06-12
2024-03-28
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References

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