1887

Abstract

Summary

History matching refers to calibration of geologic models so that they can reproduce historical production data. Automatic history matching is performed by solving inverse problems, in which production data mismatch is minimized by iteratively updating reservoir parameters in the direction of decreasing loss function. Conventional algorithms involve many reservoir simulation runs that must be performed online (during history matching), which make the process time-consuming. We have developed a novel machine learning-based approach to history matching where the goal is to learn the mapping from model input and response data during the training phase and use the trained model to generate calibrated reservoir models for a given production data. The advantage of this approach is that the time-consuming training step is performed off-line, enabling a more real-time history matching workflow with the trained model. A challenging aspect in history matching is that historical production data support only coarse-scale geologic features. Hence, without proper parameterization and regularization, the resulting inverse problems are ill-posed. Similarly, the computational efficiency is significantly improved by learning the mapping between low-dimensional representations of geologic realizations and the associated simulated production data. We demonstrate the new approach using a 2D Gaussian field and a complex 3D channelized reservoir.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201900950
2019-06-03
2020-09-21
Loading full text...

Full text loading...

References

  1. Goodfellow, I., Bengio, Y., Courville, A.
    [2016] Deep Learning. MIT Press. p.497. www.deeplearningbook.org
    [Google Scholar]
  2. Hinton, G.E., Salakhutdinov, R.
    [2006] Reducing the dimensionality of data with neural networks. Science. 313(5786), 504–507.
    [Google Scholar]
  3. Khaninezhad, M., Jafarpour, B.
    [2014] Prior model identification during subsurface flow data integration with adaptive sparse representation techniques. Comput. Geosci. pp.3–16. https://doi.org/10.1007/s10596-013-9378-7
    [Google Scholar]
  4. Narayanan, H., Mitter, S.
    [2010] Sample complexity of testing the manifold hypothesis. Advances in Neural Information Processing Systems. 23.
    [Google Scholar]
  5. Neuman, S.P.
    [2006] Blueprint for perturbative solution of flow and transport in strongly heterogeneous composite media using fractal and variational multiscale decomposition. Water Resour. Res.42, p.W06D04. https://doi.org/10.1029/2005WR004315
    [Google Scholar]
  6. Zhou, H., Gómez-Hernández, J.J., Li, L.
    [2014] Inverse methods in hydrogeology: evolution and recent trends. Adv. Water Resour. 63, 22–37. http://dx.doi.org/10.1016/j.advwatres.2013.10.014.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201900950
Loading
/content/papers/10.3997/2214-4609.201900950
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