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Features denoising-based learning for porosity classification

  • Jatin BediEmail author
  • Durga Toshniwal
IAPR-MedPRAI
  • 57 Downloads

Abstract

Reservoir characterization is one of the most challenging tasks that help in modeling different lithological properties like porosity, permeability and fluid saturation using seismic readings like velocity profile, impedance, etc. Such a model is required for field development, placing new wells and prediction management. Seismic attributes are being progressively utilized for the tasks of model building, exploration and properties estimation from the data. However, these tasks become very complex due to the nonlinear and heterogeneous nature of subsurface properties. In this context, present work proposes a recurrent neural network-based learning framework to classify porosity using seismic attributes as predictor variables. The approach begins by calculating different seismic attributes from the data. From the initially calculated attribute set, features that are to be used for classification are selected by using two different strategies. Firstly, the seismic attributes having good correlation strength with reservoir porosity are extracted. Subsequently, generative topographic map is utilized to select the significant features. The final reduced features set obtained by the integrated result of above two strategies is then fed as an input to the empirical mode decomposition (EMD) algorithm. The denoised features resulting from the EMD algorithm are used to train the classification models. Further, a comparison is carried out between the proposed classification framework \((EMD+RNN)\) and other supervised classifiers to show the performance of the proposed framework.

Keywords

Reservoir characterization Recurrent neural network Classification Petrophysical properties estimation 

List of symbols

\(s_t\)

State at timestamp t

\(W, U, V, W_\mathrm{f}, W_\mathrm{b}, W_\mathrm{o}\)

Weight parameters

\(b_\mathrm{f}, b_\mathrm{i}, b_\mathrm{i}\)

Bias parameters

\(x_t\)

Input at timestamp t

\(o_t\)

Output at timestamp t

\(c_t\)

Cell state at timestamp t

\(h_{t-1}\)

Output at previous timestamp

\(\sigma , \text {softmax}, \text {ReLU}\)

Activation functions

S(t)

Input signal

\(\nabla\)

Gradient

\(m_t\)

Average of past gradients

\(v_t\)

Average of past squared gradients

\(\beta _1, \beta _2, \theta\)

Parameters

\(m_t, v_t\)

Biased corrected parameters

\(\eta\)

Learning rate

\(\rho\)

Correlation coefficient

Notes

Acknowledgements

Funding was provided by Ministry of Human Resource Development.

Compliance with ethical standards

Conflict of interest

The author(s) declare(s) that there is no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSEIndian Institute of Technology (IIT), RoorkeeRoorkeeIndia

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