Application of Machine Learning Algorithms Using Seismic Data and Well Logs to Predict Reservoir Properties

References

1. Breiman, L.
   [2001] Random Forests. Statistics Department University of California Berkeley, 33.
   [Google Scholar]
2. Breiman, L. and Friedman, J. H.
   [1985] “Estimating Optimal Transformations for Multiple Regression and Correlation.” (with discussion) J. Amer. Statist. Assoc.80, 580.
   [Google Scholar]
3. Gorain, S. and Thakur, P.
   [2015] “Attribute based Inversion” a tool for reservoir characterization: a case study-Kalol Field, Cambay Basin, India. Acta Geodaetica et Geophysica, 50(3), 321–338.
   [Google Scholar]
4. Gorain, S.
   [2017] Application of attribute-based inversion and spectral decomposition with red-green-blue colour blending for visualization of geological features: a case study from the Kalol Field, Cambay Basin, India. Petroleum Geoscience, petgeo2015-090.
   [Google Scholar]
5. Hampson, D., Schuelke, J. and Quirein, J.
   [2001] Use of multi-attribute transforms to predict log properties from seismic data. Geophysics, 66, no. 1, 3–46.
   [Google Scholar]
6. Hampson, D. and Russell, B.
   [2005] Simultaneous inversion of pre-stack seismic data. SEG/Houston Annual Meeting, 1633–1636.
   [Google Scholar]
7. Klokov, A., Repnik, A., Bochkarev, V. and Bochkarev, A.
   [2017] Integrated Evaluatrion of Roseneath-Epsilon-Murteree Formations, Cooper Basin, Australia to Develop an Optimal Approach for Sweet Spot Determination. In Unconventional Resources Technology Conference, Austin, Texas, Society of Exploration Geophysicists, American Association of Petroleum Geologists, Society of Petroleum Engineers, pp. 1269–1278.
   [Google Scholar]
8. Kobrunov, A. and Priezzhev, I.
   [2016] Hybrid combination genetic algorithm and controlled gradient method to train a neural network, Geophysics, 81, no. 4, 1–9.
   [Google Scholar]
9. Leginowicz, A. and Pirowska, K.
   [2013] The estimation of rock properties characterizing reservoirs using genetic inversion. Nafta-Gaz, 69(5), 392–400.
   [Google Scholar]
10. Leiphartand, D.J. and Hart, B. S.
    [2001] Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico, Geophysics, 66, no. 5, 1349–1358.
    [Google Scholar]
11. Naviset, S., Morley, C. K., Naghadeh, D. H. and Ghosh, J.
    [2017] Sill emplacement during rifting and inversion from three-dimensional seismic and well data, Phitsanulok Basin, Thailand. Geosphere, 13(6).
    [Google Scholar]
12. Priezzhev, I., Shmaryan, L. and Bejarano, G.
    [2008] Non-linear multi trace seismic inversion using neural network and genetic algorithm - “Genetic Inversion”: Annual Meeting St Petersburg, EAGE, Extended Abstracts
    [Google Scholar]
13. Priezzhev, I., Scollard, A. and Lu, Z.
    [2014] Regional production prediction technology based on gravity and magnetic data from the Eagle Ford formation, Texas, USA, DenverSEG.
    [Google Scholar]
14. Tikhonov, A. N. and ArseninV. Y.
    [1977] Solutions of ill-posed problems, V H Winston and Sons, Washington D.C.
    [Google Scholar]
15. Veeken, P.C.H., Priezzhev, I.I., Shmaryan, L.E., Shteyn, Y.I., Barkov, A.Y. and Ampilov, Y.P.
    [2009] Non-linear multi-trace genetic inversion applied on seismic data across the Shtokman field (offshore northern Russia): Geophysics, 74, no. 6, 49–59.
    [Google Scholar]
16. Watanabe, S., Han, J., Hetz, G., Datta-Gupta, A., King, M. J. and Vasco, D. W.
    [2017] Streamline-Based Time-Lapse-Seismic-Data Integration Incorporating Pressure and Saturation Effects. SPE Journal.
    [Google Scholar]