1887

Abstract

Summary

A probabilistic decision support system for geosteering coupled with automated data assimilation techniques has been shown to outperform many humans at least in sandbox 2D experiments. Utilizing probabilistic decision support for optimal positioning of real wells requires a 3D probabilistic earth model compatible with standard tools, and rapid generation of synthetic logs of extra-deep measurements.

We introduce a method for representing a 3D earth model with depth uncertainty in stratigraphic surfaces. The surfaces are parameterized using a multi-scale technique, capturing correlations and uncertainties on both large and small scales. The probabilistic earth model is updated by an ensemble-based method that assimilates extra-deep EM measurements acquired while drilling. A recently developed Deep Neural Network model ensures rapid simulation of extra-deep EM measurements. However, the model assumes local layer-cake geology and thus can only interpret 1D sensitivity for each measurement position.

The proposed method is applied to a synthetic study based on geosteering in the Goliat field. The results demonstrate that data assimilation of the extra-deep EM measurements successfully reduces uncertainty in the 3D model and updates it towards the truth. Moreover, we demonstrate 3D visualization of the probabilistic model compatible with standard geo-modelling tools, useful for decision support.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2021624028
2021-11-02
2024-04-24
Loading full text...

Full text loading...

References

  1. Alyaev, S., Shahriari, M., Pardo, D., Omella, A. J., Larsen, D. S., Jahani, N., & Suter, E.
    [2021]. Modeling extra-deep EM logs using a deep neural network. Geophysics, 86(3), 1–47.
    [Google Scholar]
  2. Alyaev, S., Suter, E., Bratvold, R. B., Hong, A., Luo, X., & Fossum, K.
    [2019]. A decision support system for multi-target geosteering. Journal of Petroleum Science and Engineering, 183, 106381.
    [Google Scholar]
  3. Antonsen, F., Teixeira De Oliveira, M. E., Petersen, S. A., Metcalfe, R. W., HermanrudK., ConstableM. V., BoyleC.T., EliassenH.E., SalimD., SeydouxJ., OmeragicD.
    [2018]. Geosteering in complex mature fields through integration of 3d multi-scale LWD-data, geomodels, surface and time-lapse seismic. In SPWLA 59th Annual Logging Symposium.
    [Google Scholar]
  4. Chen, Y., & Oliver, D. S.
    [2013]. Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4), 689–703.
    [Google Scholar]
  5. Jahani, N., Garrido, J. A., Alyaev, S., Fossum, K., Suter, E., & Torres-Verdin, C.
    [2021]. Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering. Preprint submitted to Geophysics. arXiv:2103.05384.
    [Google Scholar]
  6. Larsen, D. S., Hartmann, A., Luxey, P., Martakov, S., Skillings, J., Tosi, G., & Zappalorto, L.
    [2015]. Extra-deep azimuthal resistivity for enhanced reservoir navigation in a complex reservoir in the Barents Sea. In SPE Annual Technical Conference and Exhibition.
    [Google Scholar]
  7. Wilson, G., Marchant, D., Haber, E., Clegg, N., Zurcher, D., Rawsthorne, L., & Kunnas, J.
    [2019]. Real-Time 3D Inversion of Ultra-Deep Resistivity Logging-While-Drilling Data. In SPE Annual Technical Conference and Exhibition.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.2021624028
Loading
/content/papers/10.3997/2214-4609.2021624028
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error