From Well Logs to Stratigraphic Layering: Automation, Uncertainties and Impact on Reservoir Behavior

Paul Baville and Jörg Peisker and Guillaume Caumon. ( 2019 )
in: 81st EAGE Conference and Exhibition 2019, European Association of Geoscientists & Engineers

Abstract

This paper addresses stratigraphic uncertainty and its impact on subsurface forecasts. For this, we introduce a new assisted automatic method which detects possible sequence boundaries from well log data. This method uses multi-scale signal analysis (discrete wavelet transform) to compute the probability density of finding maximum flooding surfaces and maximum regressive surfaces as a function of depth. It then recursively decomposes the studied stratigraphic section into sub-intervals where the analysis is repeated. We applied this method on a shallow marine wave dominated siliciclastic reservoir located in the Vienna Basin. We observe that several reservoir models with different stratigraphic layering (keeping all other parameters constant) have a different reservoir behavior. This allowed us to locally resolve the mismatch between measured and simulated tracer tests. This illustrates the significance of stratigraphic uncertainties in reservoir modeling and the role of automatic methods to help assess and reduce these uncertainties.

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BibTeX Reference

@INPROCEEDINGS{Baville2019EAGE,
    author = { Baville, Paul and Peisker, Jörg and Caumon, Guillaume },
     title = { From Well Logs to Stratigraphic Layering: Automation, Uncertainties and Impact on Reservoir Behavior },
     month = { "jun" },
 booktitle = { 81st EAGE Conference and Exhibition 2019 },
      year = { 2019 },
 publisher = { European Association of Geoscientists & Engineers },
       url = { https://www.earthdoc.org/content/papers/10.3997/2214-4609.201901293 },
       doi = { https://doi.org/10.3997/2214-4609.201901293 },
  abstract = { This paper addresses stratigraphic uncertainty and its impact on subsurface forecasts. For this, we introduce a new assisted automatic method which detects possible sequence boundaries from well log data. This method uses multi-scale signal analysis (discrete wavelet transform) to compute the probability density of finding maximum flooding surfaces and maximum regressive surfaces as a function of depth. It then recursively decomposes the studied stratigraphic section into sub-intervals where the analysis is repeated. We applied this method on a shallow marine wave dominated siliciclastic reservoir located in the Vienna Basin. We observe that several reservoir models with different stratigraphic layering (keeping all other parameters constant) have a different reservoir behavior. This allowed us to locally resolve the mismatch between measured and simulated tracer tests. This illustrates the significance of stratigraphic uncertainties in reservoir modeling and the role of automatic methods to help assess and reduce these uncertainties. }
}